Exactly Rao-Blackwellized Unscented Particle Filters for SLAM

摘要：

This paper addresses the limitation of the conventional Rao-Blackwellized unscented particle filters. The problem is on the usage of the overconfident optimal proposal distribution caused by perfect map assumption, so that predictive robot poses are sampled from the underestimated error covariance in the particle filtering process. The proposed solution computes more precise error covariance of the robot which contains uncertainties of the robot, map, and measurement noise. Experimental results using the benchmark dataset confirmed that the covariance of the proposed method is always larger than that of the conventional method while inducing slower increasing rate of the weight variance with less resamplings.

作者: Chanki Kim Hyoungkyun Kim Wan Kyun Chung

作者单位: Department of Mechanical Engineering at the Pohang University of Science and Technology(POSTECH),Korea

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 3589-3594

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）