Hybrid laser information improved PSO-RBPF algorithm

DOI

10.2991/icmmcce-15.2015.412How to use a DOI?

Keywords

Simultaneous localization and mapping (SLAM), Rao-Blackwellized particle filter (RBPF), particle swarm optimization (PSO), Robot operating system (ROS).

Abstract

To solve the estimator’s inconsistency in calculating the proposal distribution and the particles degeneracy phenomenon of the Rao-Blackwellized particle filter (RBPF), an improved method of RBPF based on fusing Precision laser pre-observation information particle swarm optimization (PSO) is proposed to solve simultaneous localization and mapping (SLAM) problem of mobile robot. Compared with the traditional RBPF, the improved method fuses the robot’s odometer and laser sensor information to proposal distribution computing, Meanwhile, the re-sampling process particle degeneration is applied particle swarm optimization(PSO) strategy to further optimize and adjust the obtained particle sets, The improved algorithm maintains the diversity of the particles and improves the consistency of robot's pose estimation effectively. The paper also evaluates the proposed method with the Robot Operating System (ROS), runs on platforms Pioneer3-DX robot which equipping with a URG laser sensors. The experiment results show that this method has realized online real-time high-precision grid map mapping successfully.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haibo Lin
AU  - Jingjing Ke
AU  - Yi Zhang
PY  - 2015/12
DA  - 2015/12
TI  - Hybrid laser information improved PSO-RBPF algorithm
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.412
DO  - 10.2991/icmmcce-15.2015.412
ID  - Lin2015/12
ER  -