SUN Rongchuan1,2, MA Shugen1,3, LI Bin1, WANG Minghui1, WANG Yuechao1
1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; 2. Graduate School of the Chinese Academy of Sciences, Beijing 100049, China; 3. Ritsumeikan University, Kusatsu-Shi 525-8577, Japan
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..
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.
[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.