Stereo vision based SLAM using Rao-Blackwellised particle filter

作     者：Er-yong WU Gong-yan LI Zhi-yu XIANG Ji-lin LIU 

作者机构：Department of lnformation Science and Electrical Engineering Zhejiang University Hangzhou 310027 China National Laboratory of Pattern Recognition Institute of Automation CAS Beijing 100080 China 

出 版 物：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 (浙江大学学报（英文版）A辑（应用物理与工程）)

年 卷 期：2008年第9卷第4期

页      面：500-509页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project supported by the National Natural Science Foundation of China (Nos. 60534070 and 60505017) the Science PlanningProject of Zhejiang Province (No. 2005C14008), China 

主　　题：Robot Vision based SLAM SIFT Feature management 

摘      要：We present an algorithm which can realize mobile robot in unknown outdoor environments, which 3D stereo vision simultaneous localization and mapping （SLAM） for means the 6-DOF motion and a sparse but persistent map of natural landmarks be constructed online only with a stereo camera. In mobile robotics research, we extend FastSLAM 2.0 like stereo vision SLAM with ＂pure vision＂ domain to outdoor environments. Unlike popular stochastic motion model used in conventional monocular vision SLAM, we utilize the ideas of structure from motion （SFM） for initial motion estimation, which is more suitable for the robot moving in large-scale outdoor, and textured environments. SIFT features are used as natural landmarks, and its 3D positions are constructed directly through triangulation. Considering the computational complexity and memory consumption, Bkd-tree and Best-Bin-First （BBF） search strategy are utilized for SIFT feature descriptor matching. Results show high accuracy of our algorithm, even in the circumstance of large translation and large rotation movements.