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Open AccessArticle

An Offline Coarse-To-Fine Precision Optimization Algorithm for 3D Laser SLAM Point Cloud

1
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
2
Faculty of Geomatics, East China University of Technology, Nanchang 330013, China
3
School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(20), 2352; https://doi.org/10.3390/rs11202352
Received: 4 September 2019 / Revised: 30 September 2019 / Accepted: 6 October 2019 / Published: 10 October 2019
(This article belongs to the Section Engineering Remote Sensing)
3D laser simultaneous localization and mapping (SLAM) technology is one of the most efficient methods to capture spatial information. However, the low-precision of 3D laser SLAM point cloud limits its application in many fields. In order to improve the precision of 3D laser SLAM point cloud, we presented an offline coarse-to-fine precision optimization algorithm. The point clouds are first segmented and registered at the local level. Then, a pose graph of point cloud segments is constructed using feature similarity and global registration. At last, all segments are aligned and merged into the final optimized result. In addition, a cycle based error edge elimination method is utilized to guarantee the consistency of the pose graph. The experimental results demonstrated that our algorithm achieved good performance both in our test datasets and the Cartographer public dataset. Compared with the reference data obtained by terrestrial laser scanning (TLS), the average point-to-point distance root mean square errors (RMSE) of point clouds generated by Google’s Cartographer and LOAM laser SLAM algorithms are reduced by 47.3% and 53.4% respectively after optimization in our datasets. And the average plane-to-plane distances of them are reduced by 50.9% and 52.1% respectively. View Full-Text
Keywords: LiDAR; mobile mapping; point clouds; laser SLAM; precision optimization LiDAR; mobile mapping; point clouds; laser SLAM; precision optimization
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MDPI and ACS Style

Dai, J.; Yan, L.; Liu, H.; Chen, C.; Huo, L. An Offline Coarse-To-Fine Precision Optimization Algorithm for 3D Laser SLAM Point Cloud. Remote Sens. 2019, 11, 2352.

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