Mobile robot localization using a non-linear evolutionary filter

Abstract

Abstract—This article describes a localization system for autonomous mobile robot navigation in an indoor semi-structured environment. A peripheral ring of 24 ultrasonic sensors and a camera with a motorized zoom on a pan–tilt platform are used to obtain the information required for the localization process. A non-linear � lter based on a genetic algorithm as an emerging optimization method to search for optimal positions is presented. The proposed algorithm is based upon an iterative extended Kalman � lter (EKF), which utilizes matches between observed geometric beacons and a generic map of beacon locations and the detection of arti � cial landmarks, to correct the position and orientation of the vehicle. No exhaustive map of the environment is provided to the mobile robot. It must work with a generic description of the kinds of entities in the environment. The resulting self-localization module has been integrated successfully in a more complex navigation system based on a reactive architecture. Various experimental results show the effectiveness of the presented algorithm, including a comparison with the EKF method.