Global Localization Method for Mobile Robot in Large Indoor Scene Based on the Improved Correlative Scan Matching

被引：0
|
作者：
Cheng J. [1 ]
Guo Z. [1 ]
Zhou K. [1 ]
Wang B. [1 ]
机构：
[1] China Jiliang University, Hangzhou
来源：
Jiqiren/Robot | 2023年 / 45卷 / 05期
关键词：
correlative scan matching (CSM); global localization; mobile robot; point cloud down-sampling;
D O I：
10.13973/j.cnki.robot.220309
中图分类号：
学科分类号：
摘要：
Most existing approaches to global localization are based on extracting feature points and their descriptors from raw laser scans or occupancy grid maps, and can’t extract features efficiently in some large indoor scenes with sparse environment features. For this problem, correlative scan matching (CSM) algorithm is applied to the global localization task. By calculating the contribution of points to pose solving, the CSM algorithm is improved in the point cloud down-sampling part and in the angular step-size choosing part. Finally, comparative experiments on the algorithm before and after improvement are performed in both simulation environments and real environments. Results show that, compared with the original CSM algorithm, the localization success rate of the improved algorithm is increased by 1.5%, and the time consumption is reduced by 1.1 s, which proves that the improved algorithm in this paper is more suitable for large indoor scene than the original CSM algorithm. © 2023 Chinese Academy of Sciences. All rights reserved.
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页码：581 / 590
页数：9
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