Simultaneous localization and mapping implementation based on optimized RBPF

被引：0

作者：

Luo Y. ^{[1
]}

Su Q. ^{[1
]}

Zhang Y. ^{[1
]}

Zheng X. ^{[1
]}

机构：

[1] Research Center of Information Accessibility Project and Robotics, Chongqing University of Posts and Telecommunications, Chongqing

来源：

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2016年 / 44卷 / 05期

关键词：

Hybrid proposed distribution; Pioneer3-DX robot; Resampling; Robot operation system; Simultaneous localization and mapping;

D O I：

10.13245/j.hust.160506

中图分类号：

学科分类号：

摘要：

Based on the optimized Rao-Blackwellized particle filter (RBPF), simultaneous localization and mapping (SLAM) for indoor mobile robot was realized. Combining the mobile robot's motion model and observations model, a RBPF algorithm based on the optimized hybrid proposal distribution where the annealing parameter was utilized to adjust the proportion was proposed, making the improved proposal closer to the true state distribution. An adaptive partial rank-based resampling (APRR) algorithm was designed to conquer the impoverishment of particles after resampling. The improved approach, based on the robot operation system (ROS), ran on the Pioneer3-DX robot equipped with a URG laser range finder. Experimental results show the improved algorithm can reduce required number of particles, maintain the diversity of particles, lower the computational complexity, and create high-precision 2-D grid-map online in different environments. © 2016, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.

引用

页码：30 / 34

页数：4

共 12 条

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