Robustified estimation algorithms for mobile robot localization based on geometrical environment maps

Abstract

This paper presents an improved weighted least-squares algorithm used for optimal 2D pose estimation of mobile robots navigating in real environments represented by geometrical maps. Following this map representation paradigm, feature matching is an important step in pose estimation. In this process, false feature matches may be accepted as reliable. Thus, in order to provide reliable pose estimation even in the presence of a certain level of false matches, robust M-estimators are derived. We further apply some concepts of outlier rejection for deriving a robust Kalman filter-based pose estimator. Extensive comparisons of the proposed robust methods with classic Kalman filtering-based approaches were carried out in real environments.