Research on the Improvement of Rao-Blackwellized Particle Filter for the Incremental Environment Mapping and Self-localization of a Mobile Robot
Aimed at the problem of the incremental environment mapping and self-localization of a mobile robot, the Rao-Blackwellized particle filter (RBPF) algorithm is improved to get the unite estimation of the pose of mobile robot and the position of the environmental landmarks. There are two parts in the RBPF algorithm to be studied. One is that the pose estimation of mobile robot is mended by adapting the resampling process grounded on the effective sample size (ESS) and by adopting mixture Gaussian distribution to approximate proposal distribution so as to improve the sample weight computation in obtaining ESS. The other is that the unscented Kalman filter with the adaptation estimation for the process noise is introduced into the position evaluation of the environmental landmarks. With mobile robot MORCS-1 as experimental platform, the validity of the proposed algorithm in this paper is proved.
Mobile Robot Rao-Blackwellized Particle Filter Incremental Environment Mapping and Self-localization
Liu Yanxia Yu Jinxia Cai Zixing Duan Zhuohua
College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, China College of Information Science and Engineering, Central South University, Changsha 410083, China Department of Computer, Shaoguan University, Shaoguan 512003, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
608-613
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）