Evolutionary filter for robust mobile robot global localization

Mobile robot global localization aims to determine the robot’s pose in a known environment in the absence of the robot’s initial pose information. This article presents an evolutive localization algorithm known as Evolutive Localization Filter (ELF). Based on evolutionary computation concepts, the proposed algorithm searches stochastically along the state space for the best robot’s pose estimate. The set of pose solutions (the population) represents the most likely areas according to the perception and motion information received. The population evolves by using the observation and motion error derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The resulting global localization module has been integrated successfully in a mobile robot equipped with a laser range finder. Experiments demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.