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.
 Fox D, Burgard W, Thrun S. Markov localization for mobilerobots in dynamic environments [J]. Journal of Artificial IntelligenceResearch, 1999, 11: 391-427.
 Wang C C, Thorpe C, Thrun S. Online simultaneous localizationand mapping with detection and tracking of moving objects:Theory and results from a ground vehicle in crowded ur ban areas [C]//IEEE International Conference on Robotics andAutomation. Piscataway, NJ, USA: IEEE, 2003: 842-849.
 Yang SW,Wang C C. Feasibility grids for localization and mappingin crowded urban scenes [C]//IEEE International Conferenceon Robotics and Automation. Piscataway, NJ, USA: IEEE,2011: 2322-2328.
 王炜,陈卫东,王勇.基于概率栅格地图的移动机器人可定位性估计 [J].机器人,2012,34(4): 485-491,512.Wang W, Chen W D, Wang Y. Probabilistic grid map based localizabilityestimation for mobile robots [J]. Robot, 2012, 34 (4):485-491,512.
 王卫华,陈卫东,席裕庚.移动机器人地图创建中的不确定传感信息处理 [J].自动化学报,2003,29(2): 267-274.WangWH, ChenWD, Xi Y G. Uncertainty sensor informationprocessing in map building of mobile robot [J]. Acta AutomaticaSinica, 2003, 29(2): 267-274.
 Bobrovsky B, Zakai M. A lower bound on the estimation errorfor Markov processes [J]. IEEE Transactions on AutomaticControl, 1975, 20(6): 785-788.
 Doucet A, de Freitas N D, Gordon N, et al. Sequential MonteCarlo in practice [M]. Berlin, Germany: Springer-Verlag, 2001.
 Antonelli G, Chiaverini S, Fusco G. A calibration method forodometry of mobile robots based on the least-squares technique:Theory and experimental validation [J].IEEE Transactions onRobotics, 2005, 21(5): 994-1004.
 Kleeman L. Advanced sonar and odometry error modeling forsimultaneous localisation and map building [C]//IEEE/RSJ InternationalConference on Intelligent Robots and Systems. Piscataway,NJ, USA: IEEE, 2003: 699-704.
 Roecker J A, McGillem C D. Comparison of two-sensor trackingmethods based on state vector fusion and measurement fusion [J]. IEEE Transactions on Aerospace and Electronic Systems,1988, 24(4): 447-449.
 Li Q N, Chen W D, Wang J C. Dynamic shared control forhuman-wheelchair cooperation [C]//IEEE International Conferenceon Robotics and Automation. Piscataway, NJ, USA: IEEE,2011: 4278- 4283.
 Thrun S. Robotic mapping: A survey [D]. Pittsburgh, USA:Carnegie Mellon University, 2002.