MATEC Web Conf.
Volume 160, 2018International Conference on Electrical Engineering, Control and Robotics (EECR 2018)
|Number of page(s)||7|
|Section||Intelligent Robot Design and Control|
|Published online||09 April 2018|
Simultaneous Localization and Mapping of Mobile Robot Based on Improved RBPF
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2 Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
The traditional SLAM based on RBPF has the problem of constructing high-precision map which requires large amounts of particles to make the calculation complexity and the phenomenon of particle depletion caused by particle degradation. Aiming at these problems, an improved RBPF particle filter based on adaptive bacterial foraging optimization algorithm and adaptive resampling is proposed for mobile robot SLAM problem. Firstly, the introduction of adaptive bacterial foraging algorithm to RBPF making the distribution of particles before resampling closer to the real situation. Then use the adaptive resampling method makes the newly generated particles closer to the real movement, thereby increasing the robot position estimation accuracy and map creation accuracy. The experimental results show that this method can improve the practicability of the system, reduce the computational complexity, improve the operation speed and get more effective particles while guaranteeing the accuracy of the grid map.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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