Multi-sensor fusion robust localization for indoor mobile robots based on a set-membership estimator

Abstract

Autonomous localization is a primary and crucial issue in mobile robot navigation tasks. In this article, the long-distance robust localization problem of indoor mobile robots is studied and solved by a combination style of using a laser scanner and an odometer. Firstly, a point-to-line iterative closest point(PLICP) approach is adopted to match the successive environmental information collected by a laser scanner to estimate the relative pose transformation of the robot. And then the multi-sensor fusion technology based on bounded-error set-membership estimator is proposed to to use scan matching results to correct the cumulative error of the odometer periodically to achieve precious location of the robot in indoor environments. Experimental results show that the accuracy and robustness of the proposed localization system has been improved greatly with respect to the single odometer localization approach.