Abstract
It is often the odometry accumulative error without bound after long-range movement that decreases the precision of global localization for wheeled mobile robots. Therefore, an efficient a pproach to odometry error modeling is proposed regarding gentle drive type mobile robots. The approximate functional expressions, between process input of odometry and non-systematic error as well as systematic error, are derived based on odometry error propagation law. Further, the odometry error model is applied to the global localization to compensate the accumulative error during long-time navigation. In addition, Because a lot of candidate poses of robots are generated in the process of monocular visual localization, particle swarm optimization is applied to acquire the optimal pose for mobile robots during global localization. The experiments denote that in spite of sacrificing a little computation time, the proposed method decreases odometry accumulative errors, and improves the global localization precision during autonomous navigation efficiently.
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