Efficient re-localization of mobile robot using strategy of finding a missing person

Monte Carlo localization (MCL) may fail to localize an arbitrarily moved mobile robot under extreme disturbances. Inspired by the strategy of finding a missing person (SFMP), this paper proposes an efficient relocalization solution to achieve enhanced recovery speed and success rate. With optimized distribution and number of random particle generation, the proposed SFMP-based re-localization ensures the existence of particles in where the robot appears again, which improves the robustness of MCL. First of all, a pivotal search space selection is designed to find where the robot probably appears. Then, a novel moving distance map is constructed to denote the displacement from the kidnapping point to others, thus narrowing the search space according to the possible robot moving distance. Besides, a time-varying long-short term strategy is proposed to adjust the proportion of random particles, and adaptively regulate the particle number. The effectiveness is verified by real scene experiments.