Improved Particle Filter Localization in Crowded Environments for Mobile Robots
WANG Yong1,2,3, CHEN Weidong1,2,3, WANG Jingchuan1,2,3, WANG Wei1,2,3
1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China;
3. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
In dynamic crowded environments, the localization performance of traditional map-matching algorithms for mobile robot will be significantly decreased, even the localization will completely fail, because of severe changes of the observation information. In this paper, an improved particle filter localization algorithm is proposed based on localizability estimation. On one hand, this algorithm estimates the belief of laser range finder observations using the localizability matrix of observation model. On the other hand, it estimates the belief of the odometer data using the covariance matrix of prediction model. Then based on these two indicators, the predicted robot pose is modified according to the observation information. Experiments of localization and navigation under different typical corridor environments are designed to compare the proposed algorithm with classical particle filter algorithms. The result demonstrates the validity of the proposed localization algorithm under complex environments.
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