| 摘要: |
|
| 关键词: |
| DOI： |
| Received:June 03, 2009Revised:April 16, 2010 |
| 基金项目:This work was supported by National Natural Science Foundation of China (Nos. 61075090, 61005092). |
|
| Improved Rao-Blackwellized particle filter for simultaneous robot localization and person-tracking with single mobile sensor |
| Kun QIAN,Xudong MA,Xianzhong DAI,Fang FANG |
| (Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education) |
| Abstract: |
| A probabilistic algorithm is proposed for the problem of simultaneous robot localization and peopletracking (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. |
| Key words: Mobile robot localization People tracking Rao-Blackwellized particle filter Unscented Kalman filter Service robot |
