Simultaneous Localization and Mapping of Mobile Robot Based on Improved RBPF

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...

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Published inMATEC Web of Conferences Vol. 160; p. 6002
Main Authors Zhang, Jinging, Ruan, Xiaogang, Dong, Pengfei, Zhou, Jing
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 01.01.2018
Subjects
Accuracy
Adaptive algorithms
Adaptive filters
Complexity
Resampling
Robots
Simultaneous localization and mapping
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Abstract 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.
AbstractList 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.
Author Ruan, Xiaogang
Dong, Pengfei
Zhou, Jing
Zhang, Jinging
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10.1109/TIE.2013.2270211
10.1007/978-1-4613-8997-2_14
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SubjectTerms Accuracy
Adaptive algorithms
Adaptive filters
Complexity
Resampling
Robots
Simultaneous localization and mapping
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