基于概率栅格地图的移动机器人可定位性估计

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

上海交通大学自动化系系统控制与信息处理教育部重点实验室,上海 200240

详细信息

作者简介:
王炜（1987-），男，硕士生．研究领域：服务机器人．
陈卫东（1968-），男，博士，教授，博士生导师．研究领域：智能机器人，多机器人系统．
王勇（1982-），男，博士生．研究领域：智能机器人，多机器人系统

通信作者:
陈卫东, wdchen@sjtu.edu.cn

中图分类号: TP24
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出版历程
- 收稿日期: 2011-12-14
- 修回日期: 2012-03-01
- 发布日期: 2012-07-14

Probabilistic Grid Map Based Localizability Estimation for Mobile Robots

Key Laboratory of System Control and Information Processing, Ministry of Education of China, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China

摘要

摘要: 基于广泛使用的概率栅格地图，提出了一种移动机器人可定位性估计方法．通过对定位Fisher信息矩阵进行栅格离散化，提出了静态可定位性矩阵，该矩阵适用于已知地图条件下的离线估计．在此基础上，针对在线估计中环境存在的非预期动态变化问题，采用局部感知的未知障碍物影响因子来修正静态可定位性矩阵，进而得到动态可定位性矩阵，该矩阵定量描述了机器人可定位性能力及其方向性．各种典型环境下的机器人实验结果表明了所提方法的有效性．
- 概率栅格地图 /
- 可定位性 /
- 信息矩阵 /
- 移动机器人
Abstract: Based on the widely used probabilistic grid map, a localizability estimation method for mobile robots is proposed. Firstly, the Fisher information matrix (FIM) of robot localization is transformed into discrete form, and a static localizability matrix suitable for off-line estimation based on the known grid map is obtained. On this basis, the impact factor of locally sensed unkown obstacles is adopted to modify the static localizability matrix, and a dynamic localizability matrix is proposed for on-line estimation to deal with unexpected dynamic changes of environments. This matrix describes both the localizability index and localizability direction of mobile robots quantitatively. The results of real robot experiments under different typical environments demonstrate the validity of the proposed method.
- probabilistic grid map /
- localizability /
- information matrix /
- mobile robot

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参考文献(14)

[1]	Roy N, Burgard W, Fox D, et al. Coastal navigation -- Mobile robot navigation with uncertainty in dynamic environments [C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 1999: 35-40.
[2]	Makarenko A A, Williams S B, Bourgault F, et al. An experiment in integrated exploration[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 2002: 534-539.
[3]	MacMillan N, Allen R, Marinakis D, et al. Range-based navigation system for a mobile robot[C]//Canadian Conference on Computer and Robot Vision (CRV). Piscataway, NJ, USA: IEEE, 2011: 16-23.
[4]	Fox D, Burgard W, Thrun S. Markov localization for mobile robots in dynamic environments[J]. Journal of Artificial Intelligence Research, 1999, 11(3): 391-427.
[5]	Zhou X S, Roumeliotis S I. Robot-to-robot relative pose estimation from range measurements[J]. IEEE Transactions on Robotics, 2008, 24(6): 1379-1393.
[6]	Parikh S P, Grassi Jr V, Kumar V, et al. Integrating human inputs with autonomous behaviors on an intelligent wheelchair platform[J]. IEEE Intelligent Systems, 2007, 22(2): 33-41.
[7]	Censi A. On achievable accuracy for range-finder localization[ C]//2007 IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2007: 4170-4175.
[8]	Censi A. On achievable accuracy for pose tracking[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2009: 1-7.
[9]	Diosi A, Kleeman L. Uncertainty of line segments extracted from static Sick PLS laser scans[C]//Proceedings of Australasian Conference on Robotics and Automation. Brisbane, Australia: Australasian Conference, 2003: MECSE-26-2003.
[10]	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 information processing in map building of mobile robot[J]. Acta Automatica Sinica, 2003, 29(2): 267-274.
[11]	Bobrovsky B, Zakai M. A lower bound on the estimation error for Markov processes[J]. IEEE Transactions on Automatic Control, 1975, 20(6): 785-788.
[12]	Li Q N, Chen W D, Wang J C. Dynamic shared control for human-wheelchair cooperation[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2011: 4278-4283.
[13]	Thrun S. Robotic mapping: A survey[M]. Pittsburgh, USA: Carnegie Mellon University, 2002.
[14]	Zhang Z Y. Iterative point matching for registration of free-form curves and surfaces[J]. International Journal of Computer Vision, 1994, 13(2): 119-152.