A Hybrid Self-Adaptive Particle Filter Through KLD-Sampling and SAMCL

被引：0
|
作者：
Li, Audeliano W. [1 ]
Bastos, Guilherme S. [1 ]
机构：
[1] Univ Fed Itajuba, Inst Syst Engn & Informat Technol, Itajuba, MG, Brazil
来源：
2017 18TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS (ICAR) | 2017年
关键词：
Particle filter; localization; KLD; SAMCL; LOCALIZATION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The purpose of this paper is to present a hybrid method of a particle filter for localization in mobile robotics. The main references are the particle filter based on Kullback-Leibler divergence and a self-adaptive particle filter using grid-energy. Gains and drawbacks of each method are discussed and compared with the developed algorithm. This hybrid particle filter explores the best quality of each method and the final result brings a solution to the localization problem: position tracking, global localization and kidnapping in a deterministic environment. This work was developed using the ROS framework (Robot Operating System) and tested with a Pioneer 3DX robot in real and simulation environments.
引用
收藏
页码：106 / 111
页数：6
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