Monocular-Vision-Based Mobile Robot Global Localization
LI Mao-hai1, HONG Bing-rong1, LUO Rong-hua2, CAI Ze-su1
1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China; 2. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China
厉茂海, 洪炳镕, 罗荣华, 蔡则苏. 基于单目视觉的移动机器人全局定位[J]. 机器人, 2007, 29(2): 140-144,178..
LI Mao-hai, HONG Bing-rong, LUO Rong-hua, CAI Ze-su. Monocular-Vision-Based Mobile Robot Global Localization. ROBOT, 2007, 29(2): 140-144,178..
Abstract:An environmental map built with monocular vision is used to implement mobile robot global localization.The feature matching is implemented with the KD-treebased nearest search approach.The features are extracted with Scale Invariant Feature Transform(SIFT),and discribed with highly distinctive multi-dimensional vector,making features be invariant to changes in illumination,scale,3D viewpoint and noise.A robust localization based on RANSAC(RANdom SAmple Consensus) approach is presented.Experiments on robot Pioneer 3 with monocular CCD camera in our real indoor environment show that our method is of high precision and stability.
[1] Thrun S,Bennewitz M,Burgard W,et al.Minerva:A second-generation museum tour-guide robot[A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. Piseataway,NJ,USA:IEEE,1999.1999-2005.
[2] Lowe D G.Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision,2004,60(2):91-110.
[3] Moore A W.An Introductory Tutorial on KD-trees[R]. UK:University of Cambridge,1991.
[4] 马颂德,张正友.计算机视觉--计算理论与算法基础[M]. 北京:科学出版社,1998.
[5] Fischler M A,Bolles R C.Random sample consensus:A paradigm for model fitting with application to image analysis and automated cartography[J]. Communication of the ACM,1981,24(6):381-395.