#ADDING LANDMARK
        for landmark in newLandmarks:
        #This simply creates a new row and column filled with zeros, to the covariance matrix.
            self.addLandmark(landmark)

            la = math.radians(landmark.angle)

        #This is the 2x3 jacobian of the landmark with respect to range and bearing. This as 1's along diag.
        #Here the local landmark angle gets added to the angle of the robot, stored in the state vector X.
            self.JXR[0][2] = -landmark.dist * math.sin(la + self.X[2])
            self.JXR[1][2] = landmark.dist * math.cos(la + self.X[2])

        #This is the 2x2 jacobian of the landmark with respect to the robot's state X.
            self.JZ[0][0] = math.cos(la + self.X[2])
            self.JZ[0][1] = -landmark.dist * math.sin(la + self.X[2])
            self.JZ[1][0] = math.sin(la + self.X[2])
            self.JZ[1][1] = landmark.dist * math.cos(la + self.X[2])

            #This is the 2x2 noise matrix, 'sensorAngleSigma' is equivalent to 1 degree, in radians.
            R = np.array([[landmark.dist*self.sensorDistSigma, 0],[0, self.sensorAngleSigma]])

        #This grabs (and also sets) the 2x2 covariance of the new landmark.
        #The multiples the jacobian JXR with the robot's 3x3 covariance, with JXR transposed.
        #Then the result of the noise matrix times the jacobian JZ is added.
            self.getLM(landmark.id)[:] = matmult(self.JXR, self.getRR(), self.JXR.T) + matmult(self.JZ, R, self.JZ.T)

        #Here the new bottom row is being set, as the cross-variance between the jacobian JXR and the very top row.
        #The robot-landmark cross-variances.
            self.P[3+(self.landmarkCount*2)-2:, 0:-2] = matmult(self.JXR, self.P[0:3, 0:3+(self.landmarkCount*2)-2])

        #Here the new column is set, same as above but the transposed version.
            self.P[0:-2, 3+(self.landmarkCount*2)-2:] = matmult(self.JXR, self.P[0:3, 0:3+(self.landmarkCount*2)-2]).T