Robust least squares algorithm based position and heading estimator by using range difference measurement and heading sensor

A position and heading estimator for a mobile robot is proposed with range difference (RD) measurements and a heading sensor. The estimator is developed based on the robust least squares (RoLS) algorithm and its estimation performance is improved by the heading sensor. An unbiased estimation result can be expected by the RoLS algorithm but its notable performance is restricted only when the given stochastic information of the RD measurements is correct. This aspect leads that an additional compensation procedure is required under doubtful stochastic information. To cope with this problem, we attached two transmitters on the mobile robot to obtain the position and the heading information and derived estimation errors of the position and the heading to a function of an incorrectness of the given stochastic information. The incorrectness is estimated by the extended Kalman filter (EKF) with the additional heading sensor measurements and is utilized to compensate the position and heading estimates. Through computer simulation, we verified the performance of proposition when the given stochastic information is incorrect.