Robot Localization in Rough Terrains: Performance Evaluation

Abstract

The goal of this paper is to present an overview of two common processes involved in most visual robot localization techniques: data association and robust motion estimation. For each of them, we review some of the available solutions and compare their performance in the context of outdoor robot localization, where the robot is subject to 6-DOF motion. Our experiments with different combinations of data association and motion estimation techniques show the superiority of the Hessian-Affine feature detector and the SIFT feature descriptor for data association, and the Hough Transform for robust motion estimation.