Improved Particle Filter Algorithm for Robot Localization

摘要：

For solving the problems of mobile robot SLAM (Simultaneous Localization and Mapping) in unknown environments, this paper presents an optimized RBPF algorithm. The method employs the UKF algorithm instead of the EKF algorithm to estimate landmarks, so it can avoid the derivation of complicated Jacobian Matrix and reduce the error generated by linearizing the nonlinear system. Using the Euclidean distance of particle approximate distribution to the UKF assistant proposal distribution as an adaptive particle-resampling criterion, it can avoid particles1 impoverishment and deviation to the real posterior distribution. The experimental results demonstrated these strategies can reduce the localizing complexity and enhance the algorithms real time speed and reliability.

关键词： Simultaneous Localization and Mapping Extended Kalman Filter Unseen ted Kalman Filter Particle Filter

作者: Chunlei Ji Haijun Wang Qiang Sun

作者单位: School of Electronics and Information Shanghai Dianji University Shanghai,China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

会议地点: 上海

会议语种:英文

页码: 171-174

在线出版日期: 2010-06-22（万方平台首次上网日期，不代表论文的发表时间）