Thumbnail
Access Restriction
Open

Author Peng, Gang ♦ Zheng, Wei ♦ Lu, Zezao ♦ Liao, Jinhu ♦ Hu, Lu ♦ Zhang, Gongyue ♦ He, Dingxin ♦ {"id":"U13659591","contrib_type":"Guest Editor","orcid":"http://orcid.org/0000-0002-9766-1598","surname":"Zhong","given-names":"Junpei"}
Source Hindawi
Content type Text
Publisher Hindawi
File Format PDF
Copyright Year ©2018
Language English
Abstract Adaptive Monte Carlo localization (AMCL) algorithm has a limited pose accuracy because of the nonconvexity of the laser sensor model, the complex and unstructured features of the working environment, the randomness of particle sampling, and the final pose selection problem. In this paper, an improved AMCL algorithm is proposed, aiming to build a laser radar-based robot localization system in a complex and unstructured environment, with a LIDAR point cloud scan-matching process after the particle score calculating process. The weighted mean pose of AMCL particle swarm is used as the initial pose of the scan matching process. The LIDAR point cloud is matched with the probability grid map from coarse to fine using the Gaussian-Newton method, which results in more accurate poses. Moreover, the scan-matching pose is added into the particle swarm as a high-weight particle. So the particle swarm after resampling will be more concentrated in the correct position. The particle filter and the scan-matching process form a closed loop, thus enhancing the localization accuracy of mobile robots. The experiment results demonstrate that the proposed improved AMCL algorithm is superior to the traditional AMCL algorithm in the complex and unstructured environment, by exploiting the high-accuracy characteristic of scan matching while inheriting the stability of AMCL.
ISSN 10762787
Learning Resource Type Article
Publisher Date 2018-12-11
Rights License This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e-ISSN 10990526
Journal Complexity
Volume Number 2018
Page Count 11


Open content in new tab

   Open content in new tab