SLASEM中的基于广义距离的数据关联

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孙荣川, 马书根, 李斌, 王明辉, 王越超. SLASEM中的基于广义距离的数据关联[J]. 机器人, 2011, 33(2): 208-214.. SUN Rongchuan, MA Shugen, LI Bin, WANG Minghui, WANG Yuechao. Data Association Using General Distance in SLASEM. ROBOT, 2011, 33(2): 208-214..

摘要介绍了一种同时定位与采样环境地图创建（SLASEM）中的数据关联方法.针对地图中的采样环境粒子与实际环境之间没有一一对应性、传统的马氏距离不能描述SLASEM中两个物体之间相似性的特点，提出了两个点集合之间的一种广义距离函数，并利用该距离函数进行数据关联.同时，提出了基于环境拓扑结构的方法解决多重数据关联问题，并且利用前一时刻的数据关联的结果辅助当前时刻的数据关联.最后，用两个室内环境的实验验证了所提算法的有效性.

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关键词 ：同时定位与地图创建, 同时定位与采样环境地图创建, 数据关联, 马氏距离

Abstract：A method of data association in SLASEM(simultaneous localization and sampled environment mapping) is presented.According to the features that environment samples in the map have no one-to-one correspondence with the real environment and the traditional Mahalanobis distance cannot describe the similarity of two objects in SLASEM,a general distance function between two point sets is proposed for data association.The multi-association problem is solved by utilizing the topological structure of the environment.The result of data association in previous instant is also used to aid the data association in current instant.Results of two indoor experiments validate the effectiveness of the proposed algorithm.

Key words： simultaneous localization and mapping(SLAM) simultaneous localization and sampled environment mapping(SLASEM) data association Mahalanobis distance

收稿日期: 2010-04-27 录用日期: 2010-09-25

ZTFLH:

TP24

基金资助:国家863计划资助项目（2007AA041502-5）.

通讯作者: 孙荣川 E-mail: rcsun@sia.cn

作者简介: 孙荣川（1981- ），男，博士生.研究领域：机器人导航，离散滤波，建模．
马书根（1963- ），男，博士，研究员.研究领域：仿生机器人，移动机器人，多机器人的协作与群控制．
李斌（1963- ），男，博士，研究员.研究领域：仿生机器人，移动机器人，机器人控制.

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https://robot.sia.cn/CN/ 或 https://robot.sia.cn/CN/Y2011/V33/I2/208

[1] Durrant-Whyte H E Bailey T Simultaneous localization and mapping:Part I[J].IEEE Robotics & Automation Magazine,2006,13(2):99-110.
[2] Bailey T,Durrant-Whyte H.Simultaneous localization and mapping(SLAM):PartⅡ[J].IEEE Robotics & Automation Magazine,2006,13(3):108-117.

[3] Guivant J E,Nebot E M.Optimization of the simultaneous localization and map-building algorithm for real-time implementation[J].IEEE Transactions on Robotics and Automation.2001.17(3):242-257.
[4] Martinelli A,Nguyen V,Tomatis N,et al.A relative map approach to SLAM based on shift and rotation invariants[J].Robotics and Automnomous System,2007,55(1):50-61.

[5] HahnelD,BurgardW,FoxD,et al.An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems:vol.1.Piscataway,NJ,USA:IEEE,2003:206-211.
[6] Grisetti G,Stachniss C,Burgard W.Improved techniques for grid mapping with Rao-Blackwellized particle filters[J].IEEE Transactions on Robotics,2007、23(1):34-46.
[7] Nieto J,Bailey T,Nebot E.Scan-SLAM:Combining EKFSLAM and scan correlation[C]//International Conference on Field and Service Robotics.2006:167-178.
[8] Nieto J,Guirant J,Nebot E.DenseSLAM:Simultaneous localization and dense mapping[J].International Journal of Robotics Research,2006,25(8):711-744.

[9] Sun R C,Ma S G,Li B,et al.Simultaneous localization and sampled environment mapping[C]//48th IEEE Conference on Decision and Control.USA:IEEE.2009:6484-6489.
[10] Leal J.Stochastic environment representation[D].Sydney,Australia:Sydney University,2003.
[11] Bailey T.Mobile robot localisation and mapping in extensive outdoor environments[D].Sydney,Australia:Sydney University,2002.
[12] Neira J,Tardos J D.Data association in stochastic mapping using the joint compatibility test[J].IEEE Transactions on Robotics and Automation,2001,17(6):890-897.

[13] Jensfelt P,Kristensen S.Active global localization for a mobile robot using multiple hypothesis tracking[J].IEEE Transactions on Robotics and Automation,2001,17(5):748-760.

[14] Bergstrom P.Iterative closest point method[DB/OL].(2007-11-26)[2010-03-21].http://www.mathworks.com/matlabcentral /fileexchange/12627.