An improved Monte Carlo localization using optimized iterative closest point for mobile robots

This paper details a solution of fusing combination features, Iterative Closest Point (ICP) and Monte Carlo algorithm, in order to solve the problem that mobile robot positioning is easy to fail in a dynamic environment. Firstly, an ICP algorithm based on the maximum common combination feature is pr...

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Bibliographic Details
Published in:Cognitive computation and systems 2022-03, Vol.4 (1), p.20-30
Main Authors: Ying, Wenjian, Sun, Shiyan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
auxiliary particles
combined features
Efficiency
Hypotheses
ICP algorithm
Lasers
Localization
Monte Carlo localization
proposal distribution
Registration
Research methodology
Robots
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Summary:This paper details a solution of fusing combination features, Iterative Closest Point (ICP) and Monte Carlo algorithm, in order to solve the problem that mobile robot positioning is easy to fail in a dynamic environment. Firstly, an ICP algorithm based on the maximum common combination feature is proposed to provide a more stable observation point information and therefore avoids the problem of local extremes and obtains more accurate matching results. A novel proposal distribution is then designed and auxiliary particles are used, so that the particle sets are distributed in high‐observational areas closer to the true posterior probability of the state. Finally, the experimental results on the public datasets show that the proposed algorithm is more accurate in these environments.
ISSN:2517-7567
1873-9601
2517-7567
1873-961X
DOI:10.1049/ccs2.12040