Yo Fleyt
test morning
Dependencies: ISR_Mini-explorer mbed
Fork of
roboticLab_withclass_3_July
by 
Georgios Tsamis
Diff: MiniExplorerCoimbra.cpp
- Revision:
- 9:1cc27f33d3e1
- Parent:
- 8:072a76960e27
- Child:
- 10:d0109d7cbe7c
--- a/MiniExplorerCoimbra.cpp Mon Jul 10 12:49:07 2017 +0000
+++ b/MiniExplorerCoimbra.cpp Mon Jul 10 16:23:52 2017 +0000
@@ -3,7 +3,7 @@
#define PI 3.14159
-MiniExplorerCoimbra::MiniExplorerCoimbra(float defaultXWorld, float defaultYWorld, float defaultThetaWorld, float widthRealMap, float heightRealMap):map(widthRealMap,heightRealMap,12,8),sonarLeft(10*PI/36,-4,4),sonarFront(0,0,5),sonarRight(-10*PI/36,4,4){
+MiniExplorerCoimbra::MiniExplorerCoimbra(float defaultXWorld, float defaultYWorld, float defaultThetaWorld, float widthRealMap, float heightRealMap):map(widthRealMap,heightRealMap,12,8),sonarLeft(10*PI/36,-4,4),sonarFront(0,0,5),sonarRight(-10*PI/36,4,4),covariancePositionEstimationK(3,3){
i2c1.frequency(100000);
initRobot(); //Initializing the robot
pc.baud(9600); // baud for the pc communication
@@ -31,24 +31,9 @@
this->speed=300;
this->rangeForce=50;
- //not too bad values 200 and -40000
- //not too bad values 200 and -20000
- //not too bad values 500 and -20000
- //not too bad values 500 and -25000 rangeForce 50
this->attractionConstantForce=1;
- this->repulsionConstantForce=-90;//-90 ok //idea make repulsion less divaded by distance
-
- this->covariancePositionEstimationK[0][0]=0;
- this->covariancePositionEstimationK[0][1]=0;
- this->covariancePositionEstimationK[0][2]=0;
-
- this->covariancePositionEstimationK[1][0]=0;
- this->covariancePositionEstimationK[1][1]=0;
- this->covariancePositionEstimationK[1][2]=0;
-
- this->covariancePositionEstimationK[2][0]=0;
- this->covariancePositionEstimationK[2][1]=0;
- this->covariancePositionEstimationK[2][2]=0;
+ //-90 ok with attraction impacted by distance
+ this->repulsionConstantForce=-90;
}
@@ -594,6 +579,11 @@
//this->print_map_with_robot_position();
//pc.printf("\r\nForce X repul:%f",*forceXWorld);
//pc.printf("\r\nForce Y repul:%f",*forceYWorld);
+
+ //test without atraction being impacted by distance
+ //*forceXWorld+=this->attractionConstantForce*(targetXWorld-this->xWorld);
+ //*forceYWorld+=this->attractionConstantForce*(targetYWorld-this->yWorld);
+
float distanceTargetRobot=sqrt(pow(targetXWorld-this->xWorld,2)+pow(targetYWorld-this->yWorld,2));
if(distanceTargetRobot != 0){
*forceXWorld+=this->attractionConstantForce*(targetXWorld-this->xWorld)/distanceTargetRobot;
@@ -602,6 +592,7 @@
*forceXWorld+=this->attractionConstantForce*(targetXWorld-this->xWorld)/0.001;
*forceYWorld+=this->attractionConstantForce*(targetYWorld-this->yWorld)/0.001;
}
+
//pc.printf("\r\nForce X after attract:%f",*forceXWorld);
//pc.printf("\r\nForce Y after attract:%f",*forceYWorld);
@@ -867,44 +858,7 @@
//move of targetXWorld and targetYWorld ending in a targetAngleWorld
void MiniExplorerCoimbra::go_to_point_with_angle_kalman(float targetXWorld, float targetYWorld, float targetAngleWorld) {
- float dt;
- Timer t;
- float distanceToTarget;
- //make sure the target is correct
- if(targetAngleWorld > PI)
- targetAngleWorld=-2*PI+targetAngleWorld;
- if(targetAngleWorld < -PI)
- targetAngleWorld=2*PI+targetAngleWorld;
-
- do {
- //Timer stuff
- dt = t.read();
- t.reset();
- t.start();
-
- //Updating X,Y and theta with the odometry values
- this->OdometriaKalmanFilter();
-
- //Updating motor velocities
- distanceToTarget=this->update_angular_speed_wheels_go_to_point_with_angle(targetXWorld,targetYWorld,targetAngleWorld,dt);
-
- //wait(0.2);
- //Timer stuff
- t.stop();
- pc.printf("\n\rdist to target= %f",distanceToTarget);
-
- } while(distanceToTarget>10 || (abs(targetAngleWorld-this->thetaWorld)>0.1));
-
- //Stop at the end
- leftMotor(1,0);
- rightMotor(1,0);
- pc.printf("\r\nReached Target!");
-}
-
-void MiniExplorerCoimbra::test_prediction_sonar(){
- this->map.fill_map_with_kalman_knowledge();
-
- //=====KINEMATICS===========================
+ //=====KINEMATICS===========================
float R_cm;
float L_cm;
@@ -924,55 +878,67 @@
float distanceMovedY=distanceMoved*sin(this->thetaWorld+angleMoved/2);
//try with world coordinate system
-
- float xEstimatedK=this->xWorld+distanceMovedX;
- float yEstimatedK=this->yWorld+distanceMovedY;
- float thetaWorldEstimatedK = this->thetaWorld+angleMoved;
-
- pc.printf("\r\n X=%f, Y=%f, theta=%f",xEstimatedK,yEstimatedK,thetaWorldEstimatedK);
+ myMatrix poseKplus1K(3,1);
+ poseKplus1K.data[0][0]=this->xWorld+distanceMovedX;
+ poseKplus1K.data[1][0]=this->yWorld+distanceMovedY;
+ poseKplus1K.data[2][0]=this->thetaWorld+angleMoved;
- //try with robot coordinate system
- /*
- float xEstimatedK=X;
- float yEstimatedK=Y;
- float thetaWorldEstimatedK = theta;
- */
+ pc.printf("\r\n X=%f, Y=%f, theta=%f",poseKplus1K.data[0][0],poseKplus1K.data[1][0],poseKplus1K.data[2][0]);
+
//=====ERROR_MODEL===========================
//FP Matrix
- float Fp[3][3]={{1,0,0},{0,1,0},{0,0,1}};
-
- Fp[0][2]=-1*distanceMoved*sin(this->thetaWorld+(angleMoved/2));
- Fp[1][2]=distanceMoved*cos(this->thetaWorld+(angleMoved/2));
-
+ myMatrix Fp(3,3);
+ Fp.data[0][0]=1;
+ Fp.data[1][1]=1;
+ Fp.data[2][2]=1;
+ Fp.data[0][2]=-1*distanceMoved*sin(this->thetaWorld+(angleMoved/2));
+ Fp.data[1][2]=distanceMoved*cos(this->thetaWorld+(angleMoved/2));
+
+ myMatrix FpTranspose(3,3);
+ FpTranspose.fillWithTranspose(Fp);
+
//Frl matrix
- float Frl[3][2]={{0,0},{0,0},{(1/this->distanceWheels),-(1/this->distanceWheels)}};
-
- Frl[0][0]=0.5*cos(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2));
- Frl[0][1]=0.5*cos(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2));
- Frl[1][0]=0.5*sin(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2));
- Frl[1][1]=0.5*sin(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2));
-
+ myMatrix Frl(3,2);
+ Frl.data[0][0]=0.5*cos(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2));
+ Frl.data[0][1]=0.5*cos(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2));
+ Frl.data[1][0]=0.5*sin(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2));
+ Frl.data[1][1]=0.5*sin(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2));
+ Frl.data[2][0]=(1/this->distanceWheels);
+ Frl.data[2][1]=-(1/this->distanceWheels) ;
+
+ myMatrix FrlTranspose(2,3);
+ FrlTranspose.fillWithTranspose(Frl);
+
//error constants...
float kr=1;
float kl=1;
- float covar[2][2]={{kr*abs(R_cm), 0}, {0, kl*abs(L_cm)}};
+ myMatrix covar(2,2);
+ covar.data[0][0]=kr*abs(R_cm);
+ covar.data[1][1]=kl*abs(L_cm);
//uncertainty positionx, and theta at
//1,1,1
- float Q[3][3]={{1,0,0}, {0,2,0}, {0,0,0.01}};
-
- covariancePositionEstimationK[0][0]=covar[0][0]*pow(Frl[0][0],2)+covar[1][1]*pow(Frl[0][1],2)+covariancePositionEstimationK[0][0]+Q[0][0]+covariancePositionEstimationK[2][0]*Fp[0][2]+Fp[0][2]*(covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2]);
- covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1]+covariancePositionEstimationK[2][1]*Fp[0][2]+Fp[1][2]*(covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2])+covar[0][0]*Frl[0][0]*Frl[1][0]+covar[1][1]*Frl[0][1];
- covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2]+covar[0][0]*Frl[0][0]*Frl[2][0]+covar[1][1]*Frl[0][1]*Frl[2][1];
- covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0]+covariancePositionEstimationK[2][0]*Fp[1][2]+Fp[0][2]*(covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2])+covar[0][0]*Frl[0][0]*Frl[1][0]+covar[1][1]*Frl[0][1]*Frl[1][1];
- covariancePositionEstimationK[1][1]=covar[0][0]*pow(Frl[1][0],2)+covar[1][1]*pow(Frl[1][1],2)+covariancePositionEstimationK[1][1]+Q[1][1]+covariancePositionEstimationK[2][1]*Fp[1][2]+Fp[1][2]*(covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2]);
- covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2]+covar[0][0]*Frl[1][0]*Frl[2][0]+covar[1][1]*Frl[1][1]*Frl[2][1];
- covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0]+covariancePositionEstimationK[2][2]*Fp[0][2]+covar[0][0]*Frl[0][0]*Frl[2][0]+covar[1][1]*Frl[0][1]*Frl[2][1];
- covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1]+covariancePositionEstimationK[2][2]*Fp[1][2]+covar[0][0]*Frl[1][0]*Frl[2][0]+covar[1][1]*Frl[1][1]*Frl[2][1];
- covariancePositionEstimationK[2][2]=covar[0][0]*pow(Frl[2][1],2)+covar[1][1]*pow(Frl[2][1],2)+covariancePositionEstimationK[2][2]+Q[2][2];
-
-
+ myMatrix Q(3,3);
+ Q.data[0][0]=1;
+ Q.data[1][1]=2;
+ Q.data[2][2]=0.01;
+
+ //new covariance= Fp*old covariance*FpTranspose +Frl*covar*FrlTranspose +Q
+ myMatrix covariancePositionEstimationKplus1K(3,3);
+ myMatrix temp1(3,3);
+ temp1.fillByMultiplication(Fp,this->covariancePositionEstimationK);//Fp*old covariance
+ myMatrix temp2(3,3);
+ temp2.fillByMultiplication(temp1,FpTranspose);//Fp*old covariance*FpTranspose
+ temp1.fillWithZeroes();
+ myMatrix temp3(3,2);
+ temp3.fillByMultiplication(Frl,covar);//Frl*covar
+ temp1.fillByMultiplication(temp3,FrlTranspose);//Frl*covar*FrlTranspose
+
+ covariancePositionEstimationKplus1K.addition(temp2);//Fp*old covariance*FpTranspose
+ covariancePositionEstimationKplus1K.addition(temp1);//Fp*old covariance*FpTranspose +Frl*covar*FrlTranspose
+ covariancePositionEstimationKplus1K.addition(Q);//Fp*old covariance*FpTranspose +Frl*covar*FrlTranspose +Q
+
//=====OBSERVATION=====
//get the estimated measure we should have according to our knowledge of the map and the previously computed localisation
@@ -997,7 +963,7 @@
xWorldCell=this->map.cell_width_coordinate_to_world(i);
yWorldCell=this->map.cell_height_coordinate_to_world(j);
//check left
- currDistance=this->sonarLeft.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK);
+ currDistance=this->sonarLeft.isInRange(xWorldCell,yWorldCell,poseKplus1K.data[0][0],poseKplus1K.data[1][0],poseKplus1K.data[2][0]);
if((currDistance < this->sonarLeft.maxRange) && currDistance > -1){
//check if distance is lower than previous, update lowest if so
if(currDistance < leftCmEstimated){
@@ -1007,7 +973,7 @@
}
}
//check front
- currDistance=this->sonarFront.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK);
+ currDistance=this->sonarFront.isInRange(xWorldCell,yWorldCell,poseKplus1K.data[0][0],poseKplus1K.data[1][0],poseKplus1K.data[2][0]);
if((currDistance < this->sonarFront.maxRange) && currDistance > -1){
//check if distance is lower than previous, update lowest if so
if(currDistance < frontCmEstimated){
@@ -1017,7 +983,7 @@
}
}
//check right
- currDistance=this->sonarRight.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK);
+ currDistance=this->sonarRight.isInRange(xWorldCell,yWorldCell,poseKplus1K.data[0][0],poseKplus1K.data[1][0],poseKplus1K.data[2][0]);
if((currDistance < this->sonarRight.maxRange) && currDistance > -1){
//check if distance is lower than previous, update lowest if so
if(currDistance < rightCmEstimated){
@@ -1052,28 +1018,37 @@
float innovationRight=rightCm-rightCmEstimated;
//compute jacobian of observation
- float jacobianOfObservationLeft[1][3];
- float jacobianOfObservationRight[1][3];
- float jacobianOfObservationFront[1][3];
- float xSonarLeft=xEstimatedK+this->sonarLeft.distanceX;
- float ySonarLeft=yEstimatedK+this->sonarLeft.distanceY;
+ myMatrix jacobianOfObservationLeft(1,3);
+ myMatrix jacobianOfObservationRight(1,3);
+ myMatrix jacobianOfObservationFront(1,3);
+
+ float xSonarLeft=poseKplus1K.data[0][0]+this->sonarLeft.distanceX;
+ float ySonarLeft=poseKplus1K.data[1][0]+this->sonarLeft.distanceY;
//left
- jacobianOfObservationLeft[0][0]=(xSonarLeft-xClosestCellLeft)/leftCmEstimated;
- jacobianOfObservationLeft[0][1]=(ySonarLeft-yClosestCellLeft)/leftCmEstimated;
- jacobianOfObservationLeft[0][2]=(xClosestCellLeft-xSonarLeft)*(xSonarLeft*sin(thetaWorldEstimatedK)+ySonarLeft*cos(thetaWorldEstimatedK))+(yClosestCellLeft-ySonarLeft)*(-xSonarLeft*cos(thetaWorldEstimatedK)+ySonarLeft*sin(thetaWorldEstimatedK));
+ jacobianOfObservationLeft.data[0][0]=(xSonarLeft-xClosestCellLeft)/leftCmEstimated;
+ jacobianOfObservationLeft.data[0][1]=(ySonarLeft-yClosestCellLeft)/leftCmEstimated;
+ jacobianOfObservationLeft.data[0][2]=(xClosestCellLeft-xSonarLeft)*(xSonarLeft*sin(poseKplus1K.data[2][0])+ySonarLeft*cos(poseKplus1K.data[2][0]))+(yClosestCellLeft-ySonarLeft)*(-xSonarLeft*cos(poseKplus1K.data[2][0])+ySonarLeft*sin(poseKplus1K.data[2][0]));
//front
- float xSonarFront=xEstimatedK+this->sonarFront.distanceX;
- float ySonarFront=yEstimatedK+this->sonarFront.distanceY;
- jacobianOfObservationFront[0][0]=(xSonarFront-xClosestCellFront)/frontCmEstimated;
- jacobianOfObservationFront[0][1]=(ySonarFront-yClosestCellFront)/frontCmEstimated;
- jacobianOfObservationFront[0][2]=(xClosestCellFront-xSonarFront)*(xSonarFront*sin(thetaWorldEstimatedK)+ySonarFront*cos(thetaWorldEstimatedK))+(yClosestCellFront-ySonarFront)*(-xSonarFront*cos(thetaWorldEstimatedK)+ySonarFront*sin(thetaWorldEstimatedK));
+ float xSonarFront=poseKplus1K.data[0][0]+this->sonarFront.distanceX;
+ float ySonarFront=poseKplus1K.data[1][0]+this->sonarFront.distanceY;
+ jacobianOfObservationFront.data[0][0]=(xSonarFront-xClosestCellFront)/frontCmEstimated;
+ jacobianOfObservationFront.data[0][1]=(ySonarFront-yClosestCellFront)/frontCmEstimated;
+ jacobianOfObservationFront.data[0][2]=(xClosestCellFront-xSonarFront)*(xSonarFront*sin(poseKplus1K.data[2][0])+ySonarFront*cos(poseKplus1K.data[2][0]))+(yClosestCellFront-ySonarFront)*(-xSonarFront*cos(poseKplus1K.data[2][0])+ySonarFront*sin(poseKplus1K.data[2][0]));
//right
- float xSonarRight=xEstimatedK+this->sonarRight.distanceX;
- float ySonarRight=yEstimatedK+this->sonarRight.distanceY;
- jacobianOfObservationRight[0][0]=(xSonarRight-xClosestCellRight)/rightCmEstimated;
- jacobianOfObservationRight[0][1]=(ySonarRight-yClosestCellRight)/rightCmEstimated;
- jacobianOfObservationRight[0][2]=(xClosestCellRight-xSonarRight)*(xSonarRight*sin(thetaWorldEstimatedK)+ySonarRight*cos(thetaWorldEstimatedK))+(yClosestCellRight-ySonarRight)*(-xSonarRight*cos(thetaWorldEstimatedK)+ySonarRight*sin(thetaWorldEstimatedK));
-
+ float xSonarRight=poseKplus1K.data[0][0]+this->sonarRight.distanceX;
+ float ySonarRight=poseKplus1K.data[1][0]+this->sonarRight.distanceY;
+ jacobianOfObservationRight.data[0][0]=(xSonarRight-xClosestCellRight)/rightCmEstimated;
+ jacobianOfObservationRight.data[0][1]=(ySonarRight-yClosestCellRight)/rightCmEstimated;
+ jacobianOfObservationRight.data[0][2]=(xClosestCellRight-xSonarRight)*(xSonarRight*sin(poseKplus1K.data[2][0])+ySonarRight*cos(poseKplus1K.data[2][0]))+(yClosestCellRight-ySonarRight)*(-xSonarRight*cos(poseKplus1K.data[2][0])+ySonarRight*sin(poseKplus1K.data[2][0]));
+
+ myMatrix jacobianOfObservationRightTranspose(3,1);
+ jacobianOfObservationRightTranspose.fillWithTranspose(jacobianOfObservationRight);
+
+ myMatrix jacobianOfObservationFrontTranspose(3,1);
+ jacobianOfObservationFrontTranspose.fillWithTranspose(jacobianOfObservationFront);
+
+ myMatrix jacobianOfObservationLeftTranspose(3,1);
+ jacobianOfObservationLeftTranspose.fillWithTranspose(jacobianOfObservationLeft);
//error constants 0,0,0 sonars perfect; must be found by experimenting; gives mean and standanrt deviation
//let's assume
//in centimeters
@@ -1084,9 +1059,26 @@
//R-> 4 in diagonal
//S for each sonar (concatenated covariance matrix of innovation)
- float innovationCovarianceFront=R_front+ jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]);
- float innovationCovarianceLeft=R_left + jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]);
- float innovationCovarianceRight=R_right+ jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
+ //equ (12), innovationCovariance =JacobianObservation*covariancePositionEstimationKplus1K*JacobianObservationTranspose+R
+ myMatrix temp4(1,3);
+ temp4.fillByMultiplication(jacobianOfObservationFront,covariancePositionEstimationKplus1K);
+ myMatrix temp5(1,1);
+ temp5.fillByMultiplication(temp4,jacobianOfObservationFrontTranspose);
+ float innovationCovarianceFront=temp5.data[0][0]+R_front;
+ temp4.fillWithZeroes();
+ temp5.fillWithZeroes();
+
+
+ temp4.fillByMultiplication(jacobianOfObservationLeft,covariancePositionEstimationKplus1K);
+ temp5.fillByMultiplication(temp4,jacobianOfObservationLeftTranspose);
+ float innovationCovarianceLeft=temp5.data[0][0]+R_left;
+ temp4.fillWithZeroes();
+ temp5.fillWithZeroes();
+
+
+ temp4.fillByMultiplication(jacobianOfObservationRight,covariancePositionEstimationKplus1K);
+ temp5.fillByMultiplication(temp4,jacobianOfObservationRightTranspose);
+ float innovationCovarianceRight=temp5.data[0][0]+R_right;
//check if it pass the validation gate
float gateScoreLeft=innovationLeft*innovationLeft/innovationCovarianceLeft;
@@ -1142,55 +1134,68 @@
float distanceMovedY=distanceMoved*sin(this->thetaWorld+angleMoved/2);
//try with world coordinate system
-
- float xEstimatedK=this->xWorld+distanceMovedX;
- float yEstimatedK=this->yWorld+distanceMovedY;
- float thetaWorldEstimatedK = this->thetaWorld+angleMoved;
-
- pc.printf("\r\n X=%f, Y=%f, theta=%f",xEstimatedK,yEstimatedK,thetaWorldEstimatedK);
+ myMatrix poseKplus1K(3,1);
+ poseKplus1K.data[0][0]=this->xWorld+distanceMovedX;
+ poseKplus1K.data[1][0]=this->yWorld+distanceMovedY;
+ poseKplus1K.data[2][0]=this->thetaWorld+angleMoved;
- //try with robot coordinate system
- /*
- float xEstimatedK=X;
- float yEstimatedK=Y;
- float thetaWorldEstimatedK = theta;
- */
+ pc.printf("\r\n X=%f, Y=%f, theta=%f",poseKplus1K.data[0][0],poseKplus1K.data[1][0],poseKplus1K.data[2][0]);
+
//=====ERROR_MODEL===========================
//FP Matrix
- float Fp[3][3]={{1,0,0},{0,1,0},{0,0,1}};
-
- Fp[0][2]=-1*distanceMoved*sin(this->thetaWorld+(angleMoved/2));
- Fp[1][2]=distanceMoved*cos(this->thetaWorld+(angleMoved/2));
-
+ myMatrix Fp(3,3);
+ Fp.data[0][0]=1;
+ Fp.data[1][1]=1;
+ Fp.data[2][2]=1;
+ Fp.data[0][2]=-1*distanceMoved*sin(this->thetaWorld+(angleMoved/2));
+ Fp.data[1][2]=distanceMoved*cos(this->thetaWorld+(angleMoved/2));
+
+ myMatrix FpTranspose(3,3);
+ FpTranspose.fillWithTranspose(Fp);
+
//Frl matrix
- float Frl[3][2]={{0,0},{0,0},{(1/this->distanceWheels),-(1/this->distanceWheels)}};
-
- Frl[0][0]=0.5*cos(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2));
- Frl[0][1]=0.5*cos(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2));
- Frl[1][0]=0.5*sin(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2));
- Frl[1][1]=0.5*sin(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2));
-
+ myMatrix Frl(3,2);
+ Frl.data[0][0]=0.5*cos(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2));
+ Frl.data[0][1]=0.5*cos(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*sin(this->thetaWorld+(angleMoved/2));
+ Frl.data[1][0]=0.5*sin(this->thetaWorld+(angleMoved/2))+(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2));
+ Frl.data[1][1]=0.5*sin(this->thetaWorld+(angleMoved/2))-(distanceMoved/(2*this->distanceWheels))*cos(this->thetaWorld+(angleMoved/2));
+ Frl.data[2][0]=(1/this->distanceWheels);
+ Frl.data[2][1]=-(1/this->distanceWheels) ;
+
+ myMatrix FrlTranspose(2,3);
+ FrlTranspose.fillWithTranspose(Frl);
+
//error constants...
float kr=1;
float kl=1;
- float covar[2][2]={{kr*abs(R_cm), 0}, {0, kl*abs(L_cm)}};
+ myMatrix covar(2,2);
+ covar.data[0][0]=kr*abs(R_cm);
+ covar.data[1][1]=kl*abs(L_cm);
//uncertainty positionx, and theta at
//1,1,1
- float Q[3][3]={{1,0,0}, {0,2,0}, {0,0,0.01}};
-
- covariancePositionEstimationK[0][0]=covar[0][0]*pow(Frl[0][0],2)+covar[1][1]*pow(Frl[0][1],2)+covariancePositionEstimationK[0][0]+Q[0][0]+covariancePositionEstimationK[2][0]*Fp[0][2]+Fp[0][2]*(covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2]);
- covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1]+covariancePositionEstimationK[2][1]*Fp[0][2]+Fp[1][2]*(covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2])+covar[0][0]*Frl[0][0]*Frl[1][0]+covar[1][1]*Frl[0][1];
- covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2]+covariancePositionEstimationK[2][2]*Fp[0][2]+covar[0][0]*Frl[0][0]*Frl[2][0]+covar[1][1]*Frl[0][1]*Frl[2][1];
- covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0]+covariancePositionEstimationK[2][0]*Fp[1][2]+Fp[0][2]*(covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2])+covar[0][0]*Frl[0][0]*Frl[1][0]+covar[1][1]*Frl[0][1]*Frl[1][1];
- covariancePositionEstimationK[1][1]=covar[0][0]*pow(Frl[1][0],2)+covar[1][1]*pow(Frl[1][1],2)+covariancePositionEstimationK[1][1]+Q[1][1]+covariancePositionEstimationK[2][1]*Fp[1][2]+Fp[1][2]*(covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2]);
- covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2]+covariancePositionEstimationK[2][2]*Fp[1][2]+covar[0][0]*Frl[1][0]*Frl[2][0]+covar[1][1]*Frl[1][1]*Frl[2][1];
- covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0]+covariancePositionEstimationK[2][2]*Fp[0][2]+covar[0][0]*Frl[0][0]*Frl[2][0]+covar[1][1]*Frl[0][1]*Frl[2][1];
- covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1]+covariancePositionEstimationK[2][2]*Fp[1][2]+covar[0][0]*Frl[1][0]*Frl[2][0]+covar[1][1]*Frl[1][1]*Frl[2][1];
- covariancePositionEstimationK[2][2]=covar[0][0]*pow(Frl[2][1],2)+covar[1][1]*pow(Frl[2][1],2)+covariancePositionEstimationK[2][2]+Q[2][2];
-
- //=====OBSERVATION=====
+ myMatrix Q(3,3);
+ Q.data[0][0]=1;
+ Q.data[1][1]=2;
+ Q.data[2][2]=0.01;
+
+ //new covariance= Fp*old covariance*FpTranspose +Frl*covar*FrlTranspose +Q
+ myMatrix covariancePositionEstimationKplus1K(3,3);
+ myMatrix temp1(3,3);
+ temp1.fillByMultiplication(Fp,this->covariancePositionEstimationK);//Fp*old covariance
+ myMatrix temp2(3,3);
+ temp2.fillByMultiplication(temp1,FpTranspose);//Fp*old covariance*FpTranspose
+ temp1.fillWithZeroes();
+ myMatrix temp3(3,2);
+ temp3.fillByMultiplication(Frl,covar);//Frl*covar
+ temp1.fillByMultiplication(temp3,FrlTranspose);//Frl*covar*FrlTranspose
+
+ covariancePositionEstimationKplus1K.addition(temp2);//Fp*old covariance*FpTranspose
+ covariancePositionEstimationKplus1K.addition(temp1);//Fp*old covariance*FpTranspose +Frl*covar*FrlTranspose
+ covariancePositionEstimationKplus1K.addition(Q);//Fp*old covariance*FpTranspose +Frl*covar*FrlTranspose +Q
+
+ //=====OBSERVATION=====
//get the estimated measure we should have according to our knowledge of the map and the previously computed localisation
float leftCmEstimated=this->sonarLeft.maxRange;
@@ -1209,12 +1214,12 @@
for(int i=0;i<this->map.nbCellWidth;i++){
for(int j=0;j<this->map.nbCellHeight;j++){
//check if occupied, if not discard
- if(this->map.get_proba_cell(i,j)<0.5){
+ if(this->map.get_proba_cell(i,j)>0.5){
//check if in sonar range
xWorldCell=this->map.cell_width_coordinate_to_world(i);
yWorldCell=this->map.cell_height_coordinate_to_world(j);
//check left
- currDistance=this->sonarLeft.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK);
+ currDistance=this->sonarLeft.isInRange(xWorldCell,yWorldCell,poseKplus1K.data[0][0],poseKplus1K.data[1][0],poseKplus1K.data[2][0]);
if((currDistance < this->sonarLeft.maxRange) && currDistance > -1){
//check if distance is lower than previous, update lowest if so
if(currDistance < leftCmEstimated){
@@ -1224,7 +1229,7 @@
}
}
//check front
- currDistance=this->sonarFront.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK);
+ currDistance=this->sonarFront.isInRange(xWorldCell,yWorldCell,poseKplus1K.data[0][0],poseKplus1K.data[1][0],poseKplus1K.data[2][0]);
if((currDistance < this->sonarFront.maxRange) && currDistance > -1){
//check if distance is lower than previous, update lowest if so
if(currDistance < frontCmEstimated){
@@ -1234,7 +1239,7 @@
}
}
//check right
- currDistance=this->sonarRight.isInRange(xWorldCell,yWorldCell,xEstimatedK,yEstimatedK,thetaWorldEstimatedK);
+ currDistance=this->sonarRight.isInRange(xWorldCell,yWorldCell,poseKplus1K.data[0][0],poseKplus1K.data[1][0],poseKplus1K.data[2][0]);
if((currDistance < this->sonarRight.maxRange) && currDistance > -1){
//check if distance is lower than previous, update lowest if so
if(currDistance < rightCmEstimated){
@@ -1251,6 +1256,9 @@
float leftCm = get_distance_left_sensor()/10;
float frontCm = get_distance_front_sensor()/10;
float rightCm = get_distance_right_sensor()/10;
+
+ pc.printf("\r\n1: Lcm=%f, FCm=%f, RCm=%f",leftCm,frontCm,rightCm);
+
//if superior to sonar range, put the value to sonar max range + 1
if(leftCm > this->sonarLeft.maxRange)
leftCm=this->sonarLeft.maxRange;
@@ -1263,30 +1271,40 @@
//get the innoncation: the value of the difference between the actual measure and what we anticipated
float innovationLeft=leftCm-leftCmEstimated;
float innovationFront=frontCm-frontCmEstimated;
- float innovationRight=-rightCm-rightCmEstimated;
+ float innovationRight=rightCm-rightCmEstimated;
+
//compute jacobian of observation
- float jacobianOfObservationLeft[1][3];
- float jacobianOfObservationRight[1][3];
- float jacobianOfObservationFront[1][3];
- float xSonarLeft=xEstimatedK+this->sonarLeft.distanceX;
- float ySonarLeft=yEstimatedK+this->sonarLeft.distanceY;
+ myMatrix jacobianOfObservationLeft(1,3);
+ myMatrix jacobianOfObservationRight(1,3);
+ myMatrix jacobianOfObservationFront(1,3);
+
+ float xSonarLeft=poseKplus1K.data[0][0]+this->sonarLeft.distanceX;
+ float ySonarLeft=poseKplus1K.data[1][0]+this->sonarLeft.distanceY;
//left
- jacobianOfObservationLeft[0][0]=(xSonarLeft-xClosestCellLeft)/leftCmEstimated;
- jacobianOfObservationLeft[0][1]=(ySonarLeft-yClosestCellLeft)/leftCmEstimated;
- jacobianOfObservationLeft[0][2]=(xClosestCellLeft-xSonarLeft)*(xSonarLeft*sin(thetaWorldEstimatedK)+ySonarLeft*cos(thetaWorldEstimatedK))+(yClosestCellLeft-ySonarLeft)*(-xSonarLeft*cos(thetaWorldEstimatedK)+ySonarLeft*sin(thetaWorldEstimatedK));
+ jacobianOfObservationLeft.data[0][0]=(xSonarLeft-xClosestCellLeft)/leftCmEstimated;
+ jacobianOfObservationLeft.data[0][1]=(ySonarLeft-yClosestCellLeft)/leftCmEstimated;
+ jacobianOfObservationLeft.data[0][2]=(xClosestCellLeft-xSonarLeft)*(xSonarLeft*sin(poseKplus1K.data[2][0])+ySonarLeft*cos(poseKplus1K.data[2][0]))+(yClosestCellLeft-ySonarLeft)*(-xSonarLeft*cos(poseKplus1K.data[2][0])+ySonarLeft*sin(poseKplus1K.data[2][0]));
//front
- float xSonarFront=xEstimatedK+this->sonarFront.distanceX;
- float ySonarFront=yEstimatedK+this->sonarFront.distanceY;
- jacobianOfObservationFront[0][0]=(xSonarFront-xClosestCellFront)/frontCmEstimated;
- jacobianOfObservationFront[0][1]=(ySonarFront-yClosestCellFront)/frontCmEstimated;
- jacobianOfObservationFront[0][2]=(xClosestCellFront-xSonarFront)*(xSonarFront*sin(thetaWorldEstimatedK)+ySonarFront*cos(thetaWorldEstimatedK))+(yClosestCellFront-ySonarFront)*(-xSonarFront*cos(thetaWorldEstimatedK)+ySonarFront*sin(thetaWorldEstimatedK));
+ float xSonarFront=poseKplus1K.data[0][0]+this->sonarFront.distanceX;
+ float ySonarFront=poseKplus1K.data[1][0]+this->sonarFront.distanceY;
+ jacobianOfObservationFront.data[0][0]=(xSonarFront-xClosestCellFront)/frontCmEstimated;
+ jacobianOfObservationFront.data[0][1]=(ySonarFront-yClosestCellFront)/frontCmEstimated;
+ jacobianOfObservationFront.data[0][2]=(xClosestCellFront-xSonarFront)*(xSonarFront*sin(poseKplus1K.data[2][0])+ySonarFront*cos(poseKplus1K.data[2][0]))+(yClosestCellFront-ySonarFront)*(-xSonarFront*cos(poseKplus1K.data[2][0])+ySonarFront*sin(poseKplus1K.data[2][0]));
//right
- float xSonarRight=xEstimatedK+this->sonarRight.distanceX;
- float ySonarRight=yEstimatedK+this->sonarRight.distanceY;
- jacobianOfObservationRight[0][0]=(xSonarRight-xClosestCellRight)/rightCmEstimated;
- jacobianOfObservationRight[0][1]=(ySonarRight-yClosestCellRight)/rightCmEstimated;
- jacobianOfObservationRight[0][2]=(xClosestCellRight-xSonarRight)*(xSonarRight*sin(thetaWorldEstimatedK)+ySonarRight*cos(thetaWorldEstimatedK))+(yClosestCellRight-ySonarRight)*(-xSonarRight*cos(thetaWorldEstimatedK)+ySonarRight*sin(thetaWorldEstimatedK));
-
+ float xSonarRight=poseKplus1K.data[0][0]+this->sonarRight.distanceX;
+ float ySonarRight=poseKplus1K.data[1][0]+this->sonarRight.distanceY;
+ jacobianOfObservationRight.data[0][0]=(xSonarRight-xClosestCellRight)/rightCmEstimated;
+ jacobianOfObservationRight.data[0][1]=(ySonarRight-yClosestCellRight)/rightCmEstimated;
+ jacobianOfObservationRight.data[0][2]=(xClosestCellRight-xSonarRight)*(xSonarRight*sin(poseKplus1K.data[2][0])+ySonarRight*cos(poseKplus1K.data[2][0]))+(yClosestCellRight-ySonarRight)*(-xSonarRight*cos(poseKplus1K.data[2][0])+ySonarRight*sin(poseKplus1K.data[2][0]));
+
+ myMatrix jacobianOfObservationRightTranspose(3,1);
+ jacobianOfObservationRightTranspose.fillWithTranspose(jacobianOfObservationRight);
+
+ myMatrix jacobianOfObservationFrontTranspose(3,1);
+ jacobianOfObservationFrontTranspose.fillWithTranspose(jacobianOfObservationFront);
+
+ myMatrix jacobianOfObservationLeftTranspose(3,1);
+ jacobianOfObservationLeftTranspose.fillWithTranspose(jacobianOfObservationLeft);
//error constants 0,0,0 sonars perfect; must be found by experimenting; gives mean and standanrt deviation
//let's assume
//in centimeters
@@ -1297,9 +1315,26 @@
//R-> 4 in diagonal
//S for each sonar (concatenated covariance matrix of innovation)
- float innovationCovarianceFront=R_front+ jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]);
- float innovationCovarianceLeft=R_left+ jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]);
- float innovationCovarianceRight=R_right+ jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
+ //equ (12), innovationCovariance =JacobianObservation*covariancePositionEstimationKplus1K*JacobianObservationTranspose+R
+ myMatrix temp4(1,3);
+ temp4.fillByMultiplication(jacobianOfObservationFront,covariancePositionEstimationKplus1K);
+ myMatrix temp5(1,1);
+ temp5.fillByMultiplication(temp4,jacobianOfObservationFrontTranspose);
+ float innovationCovarianceFront=temp5.data[0][0]+R_front;
+ temp4.fillWithZeroes();
+ temp5.fillWithZeroes();
+
+
+ temp4.fillByMultiplication(jacobianOfObservationLeft,covariancePositionEstimationKplus1K);
+ temp5.fillByMultiplication(temp4,jacobianOfObservationLeftTranspose);
+ float innovationCovarianceLeft=temp5.data[0][0]+R_left;
+ temp4.fillWithZeroes();
+ temp5.fillWithZeroes();
+
+
+ temp4.fillByMultiplication(jacobianOfObservationRight,covariancePositionEstimationKplus1K);
+ temp5.fillByMultiplication(temp4,jacobianOfObservationRightTranspose);
+ float innovationCovarianceRight=temp5.data[0][0]+R_right;
//check if it pass the validation gate
float gateScoreLeft=innovationLeft*innovationLeft/innovationCovarianceLeft;
@@ -1309,7 +1344,6 @@
int frontPassed=0;
int rightPassed=0;
- pc.printf("\r\n Gate scre L=%f, F=%f, R=%f",gateScoreLeft,gateScoreFront,gateScoreRight);
//5cm -> 25
int errorsquare=25;//constant error
@@ -1322,229 +1356,312 @@
//for those who passed
//compute composite innovation
int nbPassed=leftPassed+frontPassed+rightPassed;
-
- float xEstimatedKNext=xEstimatedK;
- float yEstimatedKNext=xEstimatedK;
- float thetaWorldEstimatedKNext=thetaWorldEstimatedK;
-
- float compositeInnovationCovariance3x3[3][3]={{0,0,0}, {0,0,0}, {0,0,0}};
-
- float compositeInnovationCovariance2x2[2][2]={{0,0}, {0,0}};
-
- float compositeInnovationCovariance1x1=0;
-
- float kalmanGain3X1[3][1]={{0}, {0}, {0}};
- float kalmanGain3X2[3][2]={{0,0}, {0.0}, {0,0}};
- float kalmanGain3X3[3][3]={{0,0,0}, {0,0,0}, {0,0,0}};
-
+
+ //compositeMeasurementJacobian
+ myMatrix compositeMeasurementJacobian1x3(1,3);
+ myMatrix compositeMeasurementJacobian2x3(2,3);
+ myMatrix compositeMeasurementJacobian3x3(3,3);
+
+ myMatrix compositeMeasurementJacobian1x3Transpose(3,1);
+ myMatrix compositeMeasurementJacobian2x3Transpose(3,2);
+ myMatrix compositeMeasurementJacobian3x3Transpose(3,3);
+
+ //compositeInnovation
+ myMatrix compositeInnovation3x1(3,1);
+ myMatrix compositeInnovation2x1(2,1);
+ myMatrix compositeInnovation1x1(1,1);
+
+ //compositeInnovationCovariance
+ myMatrix compositeInnovationCovariance3x3(3,3);
+ myMatrix compositeInnovationCovariance2x2(2,2);
+ myMatrix compositeInnovationCovariance1x1(1,1);
+
+ myMatrix tempCompositeInnovationCovariance3x3(3,3);
+ myMatrix tempCompositeInnovationCovariance2x2(2,2);
+
+ myMatrix compositeInnovationCovariance3x3Inverse(3,3);
+ myMatrix compositeInnovationCovariance2x2Inverse(2,2);
+ myMatrix compositeInnovationCovariance1x1Inverse(1,1);
+
+ //Kalman Gain
+ myMatrix kalmanGain3X3(3,3);
+ myMatrix kalmanGain3X2(3,2);
+ myMatrix kalmanGain3X1(3,1);
+
+ myMatrix tempKalmanGain3X3(3,3);
+ myMatrix tempKalmanGain3X2(3,2);
+ myMatrix tempKalmanGain3X1(3,1);
+
+ myMatrix kalmanGain3X3Transpose(3,3);
+ myMatrix kalmanGain3X2Transpose(2,3);
+ myMatrix kalmanGain3X1Transpose(1,3);
+
+ //new pose estimation
+ myMatrix poseKplus1Kplus1(3,1);
+ poseKplus1Kplus1.fillByCopy(poseKplus1Kplus1);
+ myMatrix tempPoseKplus1Kplus1(3,1);
+
+ //covariancePositionEstimationKplus1Kplus1
+ myMatrix covariancePositionEstimationKplus1Kplus1(3,3);
+ covariancePositionEstimationKplus1Kplus1.fillByCopy(covariancePositionEstimationKplus1K);
+
+ myMatrix temp2CovariancePositionEstimationKplus1Kplus13x3(3,3);
+ myMatrix tempCovariancePositionEstimationKplus1Kplus13x3(3,3);
+ myMatrix tempCovariancePositionEstimationKplus1Kplus13x2(3,2);
+ myMatrix tempCovariancePositionEstimationKplus1Kplus13x1(3,1);
+
//we do not use the composite measurement jacobian (16), we directly use the values from the measurement jacobian (jacobianOfObservation)
-
switch(nbPassed){
case 0://none
//nothings happens it's okay
break;
case 1://left
- compositeInnovationCovariance1x1=R_right + jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
-
- kalmanGain3X1[0][0]=(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1;
- kalmanGain3X1[1][0]=(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1;
- kalmanGain3X1[2][0]=(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1;
-
- xEstimatedKNext+= kalmanGain3X1[0][0]*innovationRight;
- yEstimatedKNext+= kalmanGain3X1[1][0]*innovationRight;
- thetaWorldEstimatedKNext+= kalmanGain3X1[2][0]*innovationRight;
+ //compute compositeMeasurementJacobian
+ //here compositeMeasurementJacobian= jacobianOfObservationLeft
+ compositeMeasurementJacobian1x3.fillByCopy(jacobianOfObservationLeft);
+ //get the compositeMeasurementJacobianTranspose
+ compositeMeasurementJacobian1x3Transpose.fillWithTranspose(compositeMeasurementJacobian1x3);
+ //compute compositeInnovation
+ //here compositeInnovation=innovationLeft
+ compositeInnovation1x1.data[0][0]=innovationLeft;
+ //compute compositeInnovationCovariance=compositeMeasurementJacobian*covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranpose
+ //add the right R on the diagonal
+ //here compositeInnovationCovariance=innovationCovarianceLeft
+ compositeInnovationCovariance1x1.data[0][0]=innovationCovarianceLeft;
+ //get the inverse of the compositeInnovationCovariance
+ compositeInnovationCovariance1x1Inverse.data[0][0]=1/innovationCovarianceLeft;
+ //compute KalmanGain=covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranspose*Inverse(compositeInnovationCovariance)
+ tempKalmanGain3X1.fillByMultiplication(covariancePositionEstimationKplus1K,compositeMeasurementJacobian1x3Transpose);
+ kalmanGain3X1.fillByMultiplication(tempKalmanGain3X1,compositeInnovationCovariance1x1Inverse);
+ //get the transpose of the kalman gain
+ kalmanGain3X1Transpose.fillWithTranspose(kalmanGain3X1);
+ //update pose estimation=old pose estimation + kalman gain*compositeInnovation
+ tempPoseKplus1Kplus1.fillByMultiplication(kalmanGain3X1,compositeInnovation1x1);
+ poseKplus1Kplus1.addition(tempPoseKplus1Kplus1);
+ //compute covariancePositionEstimationKplus1Kplus1=covariancePositionEstimationKplus1K-kalmanGain*compositeInnovationCovariance*KalmanGainTranspose
+ tempCovariancePositionEstimationKplus1Kplus13x1.fillByMultiplication(kalmanGain3X1,compositeInnovationCovariance1x1);
+ tempCovariancePositionEstimationKplus1Kplus13x3.fillByMultiplication(tempCovariancePositionEstimationKplus1Kplus13x1,kalmanGain3X1Transpose);
+ covariancePositionEstimationKplus1Kplus1.subtraction(tempCovariancePositionEstimationKplus1Kplus13x3);
- covariancePositionEstimationK[0][0]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[0][0],2) + covariancePositionEstimationK[0][0];
- covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[1][1]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[1][0],2) + covariancePositionEstimationK[1][1];
- covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[2][2]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[2][0],2) + covariancePositionEstimationK[2][2];
-
break;
case 10://front
-
- compositeInnovationCovariance1x1=R_front + jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]);
+ //compute compositeMeasurementJacobian
+ compositeMeasurementJacobian1x3.fillByCopy(jacobianOfObservationFront);
+ //get the compositeMeasurementJacobianTranspose
+ compositeMeasurementJacobian1x3Transpose.fillWithTranspose(compositeMeasurementJacobian1x3);
+ //compute compositeInnovation
+ compositeInnovation1x1.data[0][0]=innovationFront;
+ //compute compositeInnovationCovariance=compositeMeasurementJacobian*covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranpose
+ //add the right R on the diagonal
+ compositeInnovationCovariance1x1.data[0][0]=innovationCovarianceFront;
+ //get the inverse of the compositeInnovationCovariance
+ compositeInnovationCovariance1x1Inverse.data[0][0]=1/innovationCovarianceFront;
+ //compute KalmanGain=covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranspose*Inverse(compositeInnovationCovariance)
+ tempKalmanGain3X1.fillByMultiplication(covariancePositionEstimationKplus1K,compositeMeasurementJacobian1x3Transpose);
+ kalmanGain3X1.fillByMultiplication(tempKalmanGain3X1,compositeInnovationCovariance1x1Inverse);
+ //get the transpose of the kalman gain
+ kalmanGain3X1Transpose.fillWithTranspose(kalmanGain3X1);
+ //update pose estimation=old pose estimation + kalman gain*compositeInnovation
+ tempPoseKplus1Kplus1.fillByMultiplication(kalmanGain3X1,compositeInnovation1x1);
+ poseKplus1Kplus1.addition(tempPoseKplus1Kplus1);
+ //compute covariancePositionEstimationKplus1Kplus1=covariancePositionEstimationKplus1K-kalmanGain*compositeInnovationCovariance*KalmanGainTranspose
+ tempCovariancePositionEstimationKplus1Kplus13x1.fillByMultiplication(kalmanGain3X1,compositeInnovationCovariance1x1);
+ tempCovariancePositionEstimationKplus1Kplus13x3.fillByMultiplication(tempCovariancePositionEstimationKplus1Kplus13x1,kalmanGain3X1Transpose);
+ covariancePositionEstimationKplus1Kplus1.subtraction(tempCovariancePositionEstimationKplus1Kplus13x3);
- kalmanGain3X1[0][0]=(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2])/compositeInnovationCovariance1x1;
- kalmanGain3X1[1][0]=(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2])/compositeInnovationCovariance1x1;
- kalmanGain3X1[2][0]=(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2])/compositeInnovationCovariance1x1;
-
- xEstimatedKNext+= kalmanGain3X1[0][0]*innovationFront;
- yEstimatedKNext+= kalmanGain3X1[1][0]*innovationFront;
- thetaWorldEstimatedKNext+= kalmanGain3X1[2][0]*innovationFront;
-
- covariancePositionEstimationK[0][0]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[0][0],2) + covariancePositionEstimationK[0][0];
- covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[1][1]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[1][0],2) + covariancePositionEstimationK[1][1];
- covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[2][2]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[2][0],2) + covariancePositionEstimationK[2][2];
-
break;
case 11://left and front
- compositeInnovationCovariance2x2[0][0]=R_front + jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]);
- compositeInnovationCovariance2x2[0][1]=jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]);
- compositeInnovationCovariance2x2[1][0]=jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]);
- compositeInnovationCovariance2x2[1][1]=R_left + jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]);
-
- kalmanGain3X2[0][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[0][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[1][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[1][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[2][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[2][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
-
- xEstimatedKNext+= kalmanGain3X2[0][0]*innovationFront + kalmanGain3X2[0][1]*innovationLeft;
- yEstimatedKNext+= kalmanGain3X2[1][0]*innovationFront + kalmanGain3X2[1][1]*innovationLeft;
- thetaWorldEstimatedKNext+= kalmanGain3X2[2][0]*innovationFront + kalmanGain3X2[2][1]*innovationLeft;
+ //compute compositeMeasurementJacobian
+ compositeMeasurementJacobian2x3.data[0][0]=jacobianOfObservationLeft.data[0][0];
+ compositeMeasurementJacobian2x3.data[0][1]=jacobianOfObservationLeft.data[0][1];
+ compositeMeasurementJacobian2x3.data[0][2]=jacobianOfObservationLeft.data[0][2];
+
+ compositeMeasurementJacobian2x3.data[1][0]=jacobianOfObservationFront.data[0][0];
+ compositeMeasurementJacobian2x3.data[1][1]=jacobianOfObservationFront.data[0][1];
+ compositeMeasurementJacobian2x3.data[1][2]=jacobianOfObservationFront.data[0][2];
+
+ //get the compositeMeasurementJacobianTranspose
+ compositeMeasurementJacobian2x3Transpose.fillWithTranspose(compositeMeasurementJacobian2x3);
+ //compute compositeInnovation
+ compositeInnovation2x1.data[0][0]=innovationLeft;
+ compositeInnovation2x1.data[1][0]=innovationFront;
+ //compute compositeInnovationCovariance=compositeMeasurementJacobian*covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranpose+R
+ tempCompositeInnovationCovariance2x2.fillByMultiplication(compositeMeasurementJacobian2x3,covariancePositionEstimationKplus1K);
+ compositeInnovationCovariance2x2.fillByMultiplication(tempCompositeInnovationCovariance2x2,compositeMeasurementJacobian2x3Transpose);
+ //add the right R on the diagonal
+ compositeInnovationCovariance2x2.data[0][0]+=R_left;
+ compositeInnovationCovariance2x2.data[1][1]+=R_front;
+ //get the inverse of the compositeInnovationCovariance
+ compositeInnovationCovariance2x2Inverse.fillWithInverse(compositeInnovationCovariance2x2);
+ //compute KalmanGain=covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranspose*Inverse(compositeInnovationCovariance)
+ tempKalmanGain3X2.fillByMultiplication(covariancePositionEstimationKplus1K,compositeMeasurementJacobian2x3Transpose);
+ kalmanGain3X2.fillByMultiplication(tempKalmanGain3X2,compositeInnovationCovariance2x2Inverse);
+ //get the transpose of the kalman gain
+ kalmanGain3X2Transpose.fillWithTranspose(kalmanGain3X2);
+ //update pose estimation=old pose estimation + kalman gain*compositeInnovation
+ tempPoseKplus1Kplus1.fillByMultiplication(kalmanGain3X2,compositeInnovation2x1);
+ poseKplus1Kplus1.addition(tempPoseKplus1Kplus1);
+ //compute covariancePositionEstimationKplus1Kplus1=covariancePositionEstimationKplus1K-kalmanGain*compositeInnovationCovariance*KalmanGainTranspose
+ tempCovariancePositionEstimationKplus1Kplus13x2.fillByMultiplication(kalmanGain3X2,compositeInnovationCovariance2x2);
+ tempCovariancePositionEstimationKplus1Kplus13x3.fillByMultiplication(tempCovariancePositionEstimationKplus1Kplus13x2,kalmanGain3X2Transpose);
+ covariancePositionEstimationKplus1Kplus1.subtraction(tempCovariancePositionEstimationKplus1Kplus13x3);
+
+ break;
- covariancePositionEstimationK[0][0]=covariancePositionEstimationK[0][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[1][1]=covariancePositionEstimationK[1][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[2][2]=covariancePositionEstimationK[2][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]);
-
- break;
case 100://right
-
- compositeInnovationCovariance1x1=R_right + jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
-
- kalmanGain3X1[0][0]=(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1;
- kalmanGain3X1[1][0]=(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1;
- kalmanGain3X1[2][0]=(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2])/compositeInnovationCovariance1x1;
-
- xEstimatedKNext+= kalmanGain3X1[0][0]*innovationRight;
- yEstimatedKNext+= kalmanGain3X1[1][0]*innovationRight;
- thetaWorldEstimatedKNext+= kalmanGain3X1[2][0]*innovationRight;
-
- covariancePositionEstimationK[0][0]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[0][0],2) + covariancePositionEstimationK[0][0];
- covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X1[0][0]*kalmanGain3X1[1][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[1][1]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[1][0],2) + covariancePositionEstimationK[1][1];
- covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X1[0][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X1[1][0]*kalmanGain3X1[2][0]*compositeInnovationCovariance1x1;
- covariancePositionEstimationK[2][2]=- compositeInnovationCovariance1x1*pow(kalmanGain3X1[2][0],2) + covariancePositionEstimationK[2][2];
+ //compute compositeMeasurementJacobian
+ compositeMeasurementJacobian1x3.fillByCopy(jacobianOfObservationRight);
+ //get the compositeMeasurementJacobianTranspose
+ compositeMeasurementJacobian1x3Transpose.fillWithTranspose(compositeMeasurementJacobian1x3);
+ //compute compositeInnovation
+ compositeInnovation1x1.data[0][0]=innovationRight;
+ //compute compositeInnovationCovariance=compositeMeasurementJacobian*covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranpose
+ //add the right R on the diagonal
+ compositeInnovationCovariance1x1.data[0][0]=innovationCovarianceRight;
+ //get the inverse of the compositeInnovationCovariance
+ compositeInnovationCovariance1x1Inverse.data[0][0]=1/innovationCovarianceRight;
+ //compute KalmanGain=covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranspose*Inverse(compositeInnovationCovariance)
+ tempKalmanGain3X1.fillByMultiplication(covariancePositionEstimationKplus1K,compositeMeasurementJacobian1x3Transpose);
+ kalmanGain3X1.fillByMultiplication(tempKalmanGain3X1,compositeInnovationCovariance1x1Inverse);
+ //get the transpose of the kalman gain
+ kalmanGain3X1Transpose.fillWithTranspose(kalmanGain3X1);
+ //update pose estimation=old pose estimation + kalman gain*compositeInnovation
+ tempPoseKplus1Kplus1.fillByMultiplication(kalmanGain3X1,compositeInnovation1x1);
+ poseKplus1Kplus1.addition(tempPoseKplus1Kplus1);
+ //compute covariancePositionEstimationKplus1Kplus1=covariancePositionEstimationKplus1K-kalmanGain*compositeInnovationCovariance*KalmanGainTranspose
+ tempCovariancePositionEstimationKplus1Kplus13x1.fillByMultiplication(kalmanGain3X1,compositeInnovationCovariance1x1);
+ tempCovariancePositionEstimationKplus1Kplus13x3.fillByMultiplication(tempCovariancePositionEstimationKplus1Kplus13x1,kalmanGain3X1Transpose);
+ covariancePositionEstimationKplus1Kplus1.subtraction(tempCovariancePositionEstimationKplus1Kplus13x3);
break;
- case 101://right and left
- compositeInnovationCovariance2x2[0][0]=R_left + jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]);
- compositeInnovationCovariance2x2[0][1]=jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]);
- compositeInnovationCovariance2x2[1][0]=jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
- compositeInnovationCovariance2x2[1][1]=R_right + jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
-
- kalmanGain3X2[0][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[0][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[1][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[1][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[2][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[2][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
-
- xEstimatedKNext+= kalmanGain3X2[0][0]*innovationLeft + kalmanGain3X2[0][1]*innovationRight;
- yEstimatedKNext+= kalmanGain3X2[1][0]*innovationLeft + kalmanGain3X2[1][1]*innovationRight;
- thetaWorldEstimatedKNext+= kalmanGain3X2[2][0]*innovationLeft + kalmanGain3X2[2][1]*innovationRight;
-
- covariancePositionEstimationK[0][0]=covariancePositionEstimationK[0][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[1][1]=covariancePositionEstimationK[1][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[2][2]=covariancePositionEstimationK[2][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]);
-
+ case 101://left and right
+ //compute compositeMeasurementJacobian
+ compositeMeasurementJacobian2x3.data[0][0]=jacobianOfObservationLeft.data[0][0];
+ compositeMeasurementJacobian2x3.data[0][1]=jacobianOfObservationLeft.data[0][1];
+ compositeMeasurementJacobian2x3.data[0][2]=jacobianOfObservationLeft.data[0][2];
+
+ compositeMeasurementJacobian2x3.data[1][0]=jacobianOfObservationRight.data[0][0];
+ compositeMeasurementJacobian2x3.data[1][1]=jacobianOfObservationRight.data[0][1];
+ compositeMeasurementJacobian2x3.data[1][2]=jacobianOfObservationRight.data[0][2];
+
+ //get the compositeMeasurementJacobianTranspose
+ compositeMeasurementJacobian2x3Transpose.fillWithTranspose(compositeMeasurementJacobian2x3);
+ //compute compositeInnovation
+ compositeInnovation2x1.data[0][0]=innovationLeft;
+ compositeInnovation2x1.data[1][0]=innovationRight;
+ //compute compositeInnovationCovariance=compositeMeasurementJacobian*covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranpose+R
+ tempCompositeInnovationCovariance2x2.fillByMultiplication(compositeMeasurementJacobian2x3,covariancePositionEstimationKplus1K);
+ compositeInnovationCovariance2x2.fillByMultiplication(tempCompositeInnovationCovariance2x2,compositeMeasurementJacobian2x3Transpose);
+ //add the right R on the diagonal
+ compositeInnovationCovariance2x2.data[0][0]+=R_left;
+ compositeInnovationCovariance2x2.data[1][1]+=R_right;
+ //get the inverse of the compositeInnovationCovariance
+ compositeInnovationCovariance2x2Inverse.fillWithInverse(compositeInnovationCovariance2x2);
+ //compute KalmanGain=covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranspose*Inverse(compositeInnovationCovariance)
+ tempKalmanGain3X2.fillByMultiplication(covariancePositionEstimationKplus1K,compositeMeasurementJacobian2x3Transpose);
+ kalmanGain3X2.fillByMultiplication(tempKalmanGain3X2,compositeInnovationCovariance2x2Inverse);
+ //get the transpose of the kalman gain
+ kalmanGain3X2Transpose.fillWithTranspose(kalmanGain3X2);
+ //update pose estimation=old pose estimation + kalman gain*compositeInnovation
+ tempPoseKplus1Kplus1.fillByMultiplication(kalmanGain3X2,compositeInnovation2x1);
+ poseKplus1Kplus1.addition(tempPoseKplus1Kplus1);
+ //compute covariancePositionEstimationKplus1Kplus1=covariancePositionEstimationKplus1K-kalmanGain*compositeInnovationCovariance*KalmanGainTranspose
+ tempCovariancePositionEstimationKplus1Kplus13x2.fillByMultiplication(kalmanGain3X2,compositeInnovationCovariance2x2);
+ tempCovariancePositionEstimationKplus1Kplus13x3.fillByMultiplication(tempCovariancePositionEstimationKplus1Kplus13x2,kalmanGain3X2Transpose);
+ covariancePositionEstimationKplus1Kplus1.subtraction(tempCovariancePositionEstimationKplus1Kplus13x3);
+
+
break;
- case 110://right and front
-
- compositeInnovationCovariance2x2[0][0]=R_front + jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]);
- compositeInnovationCovariance2x2[0][1]=jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]);
- compositeInnovationCovariance2x2[1][0]=jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
- compositeInnovationCovariance2x2[1][1]=R_right + jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
-
- kalmanGain3X2[0][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[0][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[1][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[1][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[2][0]=(compositeInnovationCovariance2x2[1][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[1][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
- kalmanGain3X2[2][1]=(compositeInnovationCovariance2x2[0][0]*(covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]) - (compositeInnovationCovariance2x2[0][1]*(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]))/(compositeInnovationCovariance2x2[0][0]*compositeInnovationCovariance2x2[1][1] - compositeInnovationCovariance2x2[0][1]*compositeInnovationCovariance2x2[1][0]);
-
- xEstimatedKNext+= kalmanGain3X2[0][0]*innovationFront + kalmanGain3X2[0][1]*innovationRight;
- yEstimatedKNext+= kalmanGain3X2[1][0]*innovationFront + kalmanGain3X2[1][1]*innovationRight;
- thetaWorldEstimatedKNext+= kalmanGain3X2[2][0]*innovationFront + kalmanGain3X2[2][1]*innovationRight;
-
- covariancePositionEstimationK[0][0]=covariancePositionEstimationK[0][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[0][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[0][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[1][1]=covariancePositionEstimationK[1][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[1][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[1][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X2[0][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[0][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X2[1][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[1][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]);
- covariancePositionEstimationK[2][2]=covariancePositionEstimationK[2][2] - kalmanGain3X2[2][0]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][0] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][0]) - kalmanGain3X2[2][1]*(kalmanGain3X2[2][0]*compositeInnovationCovariance2x2[0][1] + kalmanGain3X2[2][1]*compositeInnovationCovariance2x2[1][1]);
-
+ case 110:// front and right
+ //compute compositeMeasurementJacobian
+ compositeMeasurementJacobian2x3.data[0][0]=jacobianOfObservationFront.data[0][0];
+ compositeMeasurementJacobian2x3.data[0][1]=jacobianOfObservationFront.data[0][1];
+ compositeMeasurementJacobian2x3.data[0][2]=jacobianOfObservationFront.data[0][2];
+
+ compositeMeasurementJacobian2x3.data[1][0]=jacobianOfObservationRight.data[0][0];
+ compositeMeasurementJacobian2x3.data[1][1]=jacobianOfObservationRight.data[0][1];
+ compositeMeasurementJacobian2x3.data[1][2]=jacobianOfObservationRight.data[0][2];
+
+ //get the compositeMeasurementJacobianTranspose
+ compositeMeasurementJacobian2x3Transpose.fillWithTranspose(compositeMeasurementJacobian2x3);
+ //compute compositeInnovation
+ compositeInnovation2x1.data[0][0]=innovationFront;
+ compositeInnovation2x1.data[1][0]=innovationRight;
+ //compute compositeInnovationCovariance=compositeMeasurementJacobian*covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranpose+R
+ tempCompositeInnovationCovariance2x2.fillByMultiplication(compositeMeasurementJacobian2x3,covariancePositionEstimationKplus1K);
+ compositeInnovationCovariance2x2.fillByMultiplication(tempCompositeInnovationCovariance2x2,compositeMeasurementJacobian2x3Transpose);
+ //add the right R on the diagonal
+ compositeInnovationCovariance2x2.data[0][0]+=R_front;
+ compositeInnovationCovariance2x2.data[1][1]+=R_right;
+ //get the inverse of the compositeInnovationCovariance
+ compositeInnovationCovariance2x2Inverse.fillWithInverse(compositeInnovationCovariance2x2);
+ //compute KalmanGain=covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranspose*Inverse(compositeInnovationCovariance)
+ tempKalmanGain3X2.fillByMultiplication(covariancePositionEstimationKplus1K,compositeMeasurementJacobian2x3Transpose);
+ kalmanGain3X2.fillByMultiplication(tempKalmanGain3X2,compositeInnovationCovariance2x2Inverse);
+ //get the transpose of the kalman gain
+ kalmanGain3X2Transpose.fillWithTranspose(kalmanGain3X2);
+ //update pose estimation=old pose estimation + kalman gain*compositeInnovation
+ tempPoseKplus1Kplus1.fillByMultiplication(kalmanGain3X2,compositeInnovation2x1);
+ poseKplus1Kplus1.addition(tempPoseKplus1Kplus1);
+ //compute covariancePositionEstimationKplus1Kplus1=covariancePositionEstimationKplus1K-kalmanGain*compositeInnovationCovariance*KalmanGainTranspose
+ tempCovariancePositionEstimationKplus1Kplus13x2.fillByMultiplication(kalmanGain3X2,compositeInnovationCovariance2x2);
+ tempCovariancePositionEstimationKplus1Kplus13x3.fillByMultiplication(tempCovariancePositionEstimationKplus1Kplus13x2,kalmanGain3X2Transpose);
+ covariancePositionEstimationKplus1Kplus1.subtraction(tempCovariancePositionEstimationKplus1Kplus13x3);
+
break;
- case 111://right front and left
- //get the compositeInnovationCovariance3x3
- compositeInnovationCovariance3x3[0][0]=R_front + jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]);
- compositeInnovationCovariance3x3[0][1]=jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]);
- compositeInnovationCovariance3x3[0][2]=jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]);
- compositeInnovationCovariance3x3[1][0]=jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]);
- compositeInnovationCovariance3x3[1][1]=R_left + jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]);
- compositeInnovationCovariance3x3[1][2]=jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]);
- compositeInnovationCovariance3x3[2][0]=jacobianOfObservationFront[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationFront[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationFront[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
- compositeInnovationCovariance3x3[2][1]=jacobianOfObservationLeft[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationLeft[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationLeft[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
- compositeInnovationCovariance3x3[2][2]=R_right + jacobianOfObservationRight[0][0]*(covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][1]*(covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][2]) + jacobianOfObservationRight[0][2]*(covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]);
-
- //compute the kalman gain
- kalmanGain3X3[0][0]=(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]);
- kalmanGain3X3[0][1]=-(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]);
- kalmanGain3X3[0][2]=(covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[0][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[0][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[0][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[0][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[0][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[0][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[0][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[0][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[0][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]);
- kalmanGain3X3[1][0]=(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]);
- kalmanGain3X3[1][1]=-(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]);
- kalmanGain3X3[1][2]=(covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[1][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[1][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[1][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[1][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[1][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[1][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[1][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[1][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[1][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]);
- kalmanGain3X3[2][0]=(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]);
- kalmanGain3X3[2][1]=-(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][2] - covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] - covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][2] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[2][1] - covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[2][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]);
- kalmanGain3X3[2][2]=(covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[2][0]*jacobianOfObservationFront[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[2][1]*jacobianOfObservationFront[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] + covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2] - covariancePositionEstimationK[2][2]*jacobianOfObservationFront[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[2][0]*jacobianOfObservationLeft[0][0]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[2][1]*jacobianOfObservationLeft[0][1]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] - covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2] + covariancePositionEstimationK[2][2]*jacobianOfObservationLeft[0][2]*compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[2][0]*jacobianOfObservationRight[0][0]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[2][1]*jacobianOfObservationRight[0][1]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0] + covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1] - covariancePositionEstimationK[2][2]*jacobianOfObservationRight[0][2]*compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0])/(compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][2] - compositeInnovationCovariance3x3[0][0]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][2] + compositeInnovationCovariance3x3[0][1]*compositeInnovationCovariance3x3[1][2]*compositeInnovationCovariance3x3[2][0] + compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][0]*compositeInnovationCovariance3x3[2][1] - compositeInnovationCovariance3x3[0][2]*compositeInnovationCovariance3x3[1][1]*compositeInnovationCovariance3x3[2][0]);
-
- //update the prediction
- xEstimatedKNext+= kalmanGain3X3[0][0]*innovationFront + kalmanGain3X3[0][1]*innovationLeft + kalmanGain3X3[0][2]*innovationRight;
- yEstimatedKNext+= kalmanGain3X3[1][0]*innovationFront + kalmanGain3X3[1][1]*innovationLeft + kalmanGain3X3[1][2]*innovationRight;
- thetaWorldEstimatedKNext+= kalmanGain3X3[2][0]*innovationFront + kalmanGain3X3[2][1]*innovationLeft + kalmanGain3X3[2][2]*innovationRight;
-
- //update covariancePositionEstimationK
- covariancePositionEstimationK[0][0]=covariancePositionEstimationK[0][0] - kalmanGain3X3[0][0]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[0][1]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[0][2]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][2]);
- covariancePositionEstimationK[0][1]=covariancePositionEstimationK[0][1] - kalmanGain3X3[1][0]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[1][1]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[1][2]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][2]);
- covariancePositionEstimationK[0][2]=covariancePositionEstimationK[0][2] - kalmanGain3X3[2][0]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[2][1]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[2][2]*(kalmanGain3X3[0][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[0][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[0][2]*compositeInnovationCovariance3x3[2][2]);
- covariancePositionEstimationK[1][0]=covariancePositionEstimationK[1][0] - kalmanGain3X3[0][0]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[0][1]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[0][2]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][2]);
- covariancePositionEstimationK[1][1]=covariancePositionEstimationK[1][1] - kalmanGain3X3[1][0]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[1][1]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[1][2]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][2]);
- covariancePositionEstimationK[1][2]=covariancePositionEstimationK[1][2] - kalmanGain3X3[2][0]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[2][1]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[2][2]*(kalmanGain3X3[1][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[1][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[1][2]*compositeInnovationCovariance3x3[2][2]);
- covariancePositionEstimationK[2][0]=covariancePositionEstimationK[2][0] - kalmanGain3X3[0][0]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[0][1]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[0][2]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][2]);
- covariancePositionEstimationK[2][1]=covariancePositionEstimationK[2][1] - kalmanGain3X3[1][0]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[1][1]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[1][2]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][2]);
- covariancePositionEstimationK[2][2]=covariancePositionEstimationK[2][2] - kalmanGain3X3[2][0]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][0] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][0] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][0]) - kalmanGain3X3[2][1]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][1] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][1] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][1]) - kalmanGain3X3[2][2]*(kalmanGain3X3[2][0]*compositeInnovationCovariance3x3[0][2] + kalmanGain3X3[2][1]*compositeInnovationCovariance3x3[1][2] + kalmanGain3X3[2][2]*compositeInnovationCovariance3x3[2][2]);
-
+ case 111://left front and right
+ //compute compositeMeasurementJacobian
+ compositeMeasurementJacobian3x3.data[0][0]=jacobianOfObservationLeft.data[0][0];
+ compositeMeasurementJacobian3x3.data[0][1]=jacobianOfObservationLeft.data[0][1];
+ compositeMeasurementJacobian3x3.data[0][2]=jacobianOfObservationLeft.data[0][2];
+
+ compositeMeasurementJacobian3x3.data[1][0]=jacobianOfObservationFront.data[0][0];
+ compositeMeasurementJacobian3x3.data[1][1]=jacobianOfObservationFront.data[0][1];
+ compositeMeasurementJacobian3x3.data[1][2]=jacobianOfObservationFront.data[0][2];
+
+ compositeMeasurementJacobian3x3.data[2][0]=jacobianOfObservationRight.data[0][0];
+ compositeMeasurementJacobian3x3.data[2][1]=jacobianOfObservationRight.data[0][1];
+ compositeMeasurementJacobian3x3.data[2][2]=jacobianOfObservationRight.data[0][2];
+
+ //get the compositeMeasurementJacobianTranspose
+ compositeMeasurementJacobian3x3Transpose.fillWithTranspose(compositeMeasurementJacobian3x3);
+ //compute compositeInnovation
+ compositeInnovation3x1.data[0][0]=innovationLeft;
+ compositeInnovation3x1.data[1][0]=innovationFront;
+ compositeInnovation3x1.data[2][0]=innovationRight;
+ //compute compositeInnovationCovariance=compositeMeasurementJacobian*covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranpose+R
+ tempCompositeInnovationCovariance3x3.fillByMultiplication(compositeMeasurementJacobian3x3,covariancePositionEstimationKplus1K);
+ compositeInnovationCovariance3x3.fillByMultiplication(tempCompositeInnovationCovariance3x3,compositeMeasurementJacobian3x3Transpose);
+ //add the right R on the diagonal
+ compositeInnovationCovariance3x3.data[0][0]+=R_left;
+ compositeInnovationCovariance3x3.data[1][1]+=R_front;
+ compositeInnovationCovariance3x3.data[2][2]+=R_right;
+ //get the inverse of the compositeInnovationCovariance
+ compositeInnovationCovariance3x3Inverse.fillWithInverse(compositeInnovationCovariance3x3);
+ //compute KalmanGain=covariancePositionEstimationKplus1K*compositeMeasurementJacobianTranspose*Inverse(compositeInnovationCovariance)
+ tempKalmanGain3X3.fillByMultiplication(covariancePositionEstimationKplus1K,compositeMeasurementJacobian3x3Transpose);
+ kalmanGain3X3.fillByMultiplication(tempKalmanGain3X3,compositeInnovationCovariance3x3Inverse);
+ //get the transpose of the kalman gain
+ kalmanGain3X3Transpose.fillWithTranspose(kalmanGain3X3);
+ //update pose estimation=old pose estimation + kalman gain*compositeInnovation
+ tempPoseKplus1Kplus1.fillByMultiplication(kalmanGain3X3,compositeInnovation3x1);
+ poseKplus1Kplus1.addition(tempPoseKplus1Kplus1);
+ //compute covariancePositionEstimationKplus1Kplus1=covariancePositionEstimationKplus1K-kalmanGain*compositeInnovationCovariance*KalmanGainTranspose
+ temp2CovariancePositionEstimationKplus1Kplus13x3.fillByMultiplication(kalmanGain3X3,compositeInnovationCovariance3x3);
+ tempCovariancePositionEstimationKplus1Kplus13x3.fillByMultiplication(temp2CovariancePositionEstimationKplus1Kplus13x3,kalmanGain3X3Transpose);
+ covariancePositionEstimationKplus1Kplus1.subtraction(tempCovariancePositionEstimationKplus1Kplus13x3);
+
break;
}
- //big question, in wich coordinate space are those measurements...
- //try with world coordinate system
+ //update covariancePositionEstimationK =covariancePositionEstimationKplus1Kplus1
+ this->covariancePositionEstimationK.fillByCopy(covariancePositionEstimationKplus1Kplus1);
- this->xWorld=xEstimatedKNext;
- this->yWorld=yEstimatedKNext;
- this->thetaWorld=thetaWorldEstimatedKNext;
+ //update pose
+ this->xWorld=poseKplus1Kplus1.data[0][0];
+ this->yWorld=poseKplus1Kplus1.data[1][0];
+ this->thetaWorld=poseKplus1Kplus1.data[2][0];
- pc.printf("\r\nAfter Kalm X=%f, Y=%f, theta=%f",xEstimatedKNext,yEstimatedKNext,thetaWorldEstimatedKNext);
+ pc.printf("\r\nAfter Kalm X=%f, Y=%f, theta=%f",xWorld,yWorld,thetaWorld);
//try with robot one
/*
X=xEstimatedKNext;