Open AccessCorrelation scan matching algorithm based on multi‐resolution auxiliary historical point cloud and lidar simultaneous localisation and mapping positioning application
Author(s) -
Liu Haiqiao,
Luo Shibin,
Lu Jiazhen
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1657
Subject(s) - computer science , matching (statistics) , computation , point cloud , algorithm , lidar , blossom algorithm , frame (networking) , iterative closest point , position (finance) , computer vision , artificial intelligence , mathematics , remote sensing , geography , telecommunications , statistics , finance , economics
The matching algorithm is an important part of simultaneous location and mapping. Aiming at the problem of large computation and poor real‐time performance of two‐dimensional lidar traditional correlation scan matching (CSM) algorithm, a multi‐resolution auxiliary historical point cloud matching algorithm is proposed, which combines high and low resolution and adopts a single‐frame to multi‐frame step‐by‐step matching scheme. The algorithm was carried out on the sweeping robot. Compared with the traditional CSM algorithm and iterative closest points algorithm, the single position accuracy of the method in this study is improved. In the indoor space of ∼10 m × 10 m, the cumulative error is reduced by 16.24 and 33.96%, respectively. Consequently, our algorithm can still manage to process in real‐time.