In the robot navigation problem, noisy sensor data must be filtered to obtain the best estimate of the robot position. We propose using a recursive total least squares algorithm to obtain estimates of the robot position. We avoid several weaknesses inherent in the use of the Kalman and extended Kalman filters, achieving much faster convergence without good initial (a priori) estimates of the position. The performance of the method is illustrated both by simulation and on an actual mobile robot with a camera.
['Daniel Boley', 'Erik S. Steinmetz', 'Karen T. Sutherland']
Robot localization from landmarks using recursive total least squares