Open AccessAn improved Monte Carlo localization using optimized iterative closest point for mobile robots
Author(s) -
Ying Wenjian,
Sun Shiyan
Publication year - 2022
Publication title -
cognitive computation and systems
Language(s) - English
Resource type - Journals
ISSN - 2517-7567
DOI - 10.1049/ccs2.12040
Subject(s) - iterative closest point , monte carlo method , computer science , mobile robot , monte carlo localization , matching (statistics) , point (geometry) , feature (linguistics) , particle filter , algorithm , iterative method , robot , mathematical optimization , artificial intelligence , mathematics , point cloud , kalman filter , statistics , linguistics , philosophy , geometry
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.