Distributed-Perception-Based Simultaneous Localization and Mapping for Mobile Robots
LIANG Zhi-wei, MA Xu-dong, DAI Xian-zhong, FANG Fang
Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China
Abstract：This paper presents a two-level simultaneous localization and mapping (SLAM) method based on distributed perception that allows us to obtain accurate grid maps of large environments.At local map level,a new local map is built based on information from the robot laser sensor and odometry using a Rao-Blackwellized particle filter (RBPF) method once the robot enters a new camera visual field.Meanwhile,we also solve the camera-calibration problem by using a marker attached to the robot which moves in a curve fashion in the camera visual field.The global level is an adjacency graph whose arcs are labeled with the constraints between local maps.To obtain an accurate and globally consistent map,a stochastic gradient descent (SGD) algorithm is employed to optimize the existed adjacency graph.Experimental results illustrate the validity of the presented approach.
[1] Thrun S.Fox D,Burgard W,et al.Robust Monte Carlo localization for mobile robots[Jl.Artificial Intelligence,2001,128(1-2):99～141.
[2] Fox D.Adapting the sample size in particle filters through KLDsampling[J].The International Journal of Robotics Research,2003,22(12):985～1003.
[3] 厉茂海,洪炳熔.移动机器人的概率定位方法研究进展[J].机器人,2005,27(4):380～384.Li Mao-hai,Hong Bing-rong.Progress of probabilistic localization methods in mobile robots[J].Robot,2005,27(4):380～384.
[4] 梁志伟,马旭东,戴先中.分布式感知协作的扩展Monte Carlo定位算法[J].机器人,2008,30(3):210～216.Liang Zhi-wei,Ma Xu-dong,Dai Xian-zhong.An extended Monte Carlo localization approach based on collaborative distribution perception[J].Robot,2008,30(3):210～216.
[5] Murphy K.Bayesian map learning in dynamoic environments[A].Advances in Neural Information Processing Systems (NIPS)[C].Denver,USA:1999.1015～1021.
[6] Montemerlo M,Thrun S,Koller D,et al.FastSLAM 2.0:An improved particle filtering algorithm for simultaneous localization and mapping that provably converges[A].Proceedings of the International Joint Conference on Artificial Intelligence[C].Acapulco,Mexico:2003.1151～1156.
[7] Hahnei D,Burgard W,Fox D,et al.An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements[A].Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems[C].Piscataway,NJ,USA:IEEE,2003.206～211.
[8] 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.
[9] Ho K L,Newman E Detecting loop closure with scene sequences[J].International Journal of Computer Vision,2007,74(3):261～286.
[10] Liang Z W,Ma X D,Dai X Z.Information-theoretic approaches based on sequential Monte Carlo to collaborative distributed sensors for mobile robot localization[J].Journal of Intelligent and Robotic Systems,2008,52(2):157～174.
[11] Grisetti G,Stachniss C,Grzonka S,et al.A tree parameterization for efficiently computing maximum likelihood maps using gradient descent[Al.Proceedings of Robotics:Science and Systems (RSS)[C].Atlanta,GA,USA:2007.
[12] Robbins H,Monro S.A stochastic approximation method[J].Annals of Mathematical Statistics,1951,22(3):400～407.
[13] CARMEN Team.Carmen Robot Navigation Toolkit[EB/OL].http://carmen.sourceforge.net,2008.
