Robust mobile robot on-the-fly localization: using geometrical feature matching method

Self-localization is an essential component of any autonomous mobile robot system. This paper presents a fast and robust feature-based approach to mobile robot on-the-fly global localization in dynamic environments. A robust feature extraction and correspondence algorithm was introduced to extract the landmarks of the environment and to triangulate the robot´s positions. The approach take into account the hard real time constraint and the partial observability of the environment. On-line localization is performed by matching candidate landmarks from the robot´s local view to the tracked landmarks. Experimental validation in the context of middle size league RoboCup was carried out in a real environment with the mobile robot Jiaolong which equips a laser range finder (LRF).