Efficient re-localization of mobile robot using strategy of finding a missing person. (May 2021)

• Record Type: Journal Article
• Authors: Meng, Jie; Wang, Shuting; Xie, Yuanlong; Jiang, Liquan; Li, Gen; Liu, Chao
• Is Part Of: Measurement. Volume 176(2021)
• Publication Date: 2021-05
• Subjects: Monte-Carlo localization -- Mobile robot -- Re-localization -- Moving distance map -- Strategy of finding a missing person; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109212
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An efficient re-localization based on SFMP is proposed. A pivotal search space selection method using topological map is designed. A novel moving distance map is constructed. A time-varying long-short term strategy is presented. Abstract: 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 re-localization 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.