Journal of Control Theory and Applications

, Volume 9, Issue 4, pp 472–478 | Cite as

Improved Rao-Blackwellized particle filter for simultaneous robot localization and person-tracking with single mobile sensor

  • Kun QianEmail author
  • Xudong Ma
  • Xianzhong Dai
  • Fang Fang


A probabilistic algorithm is proposed for the problem of simultaneous robot localization and people-tracking (SLAP) using single onboard sensor in situations with sensor noise and global uncertainties over the observer’s pose. By the decomposition of the joint distribution according to the Rao-Blackwell theorem, posteriors of the robot pose are sequentially estimated over time by a smoothed laser perception model and an improved resampling scheme with evolution strategies; the conditional distribution of the person’s position is estimated using unscented Kalman filter (UKF) to deal with the nonlinear dynamic of human motion. Experiments conducted in a real indoor service robot scenario validate the favorable performance of the positional accuracy as well as the improved computational efficiency.


Mobile robot localization People tracking Rao-Blackwellized particle filter Unscented Kalman filter Service robot 


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Copyright information

© South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of EducationNanjing JiangsuChina

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