Learning from human cognition: Collaborative localization for vision-based autonomous robots

This paper presents a novel approach for a group of vision-based autonomous robots to localize in dynamic environments. We propose a hybrid system method for localization consisted of on-line and off-line subsystems inspired by human cognition. For the on-line subsystem, we use the landmark based Markov localization method to estimate the position. When the robot does not update the probability of current position through landmarks for a certain period, we use the off-line experience subsystem to help. In addition to the hybrid system for individual localization, we propose a method of dynamic reference object for collaborative localization. By using this method, an autonomous robot can estimate and correct its position perception more accurately and effectively, taking the odometry error and other negative influence into consideration. Satisfactory experimental results are obtained in the RoboCup Four-Legged League environment.