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

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 pres...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 11; no. 20; p. 2352
Main Authors Dai, Jicheng, Yan, Li, Liu, Hua, Chen, Changjun, Huo, Liang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2019
Subjects
Accuracy
Algorithms
Cartography
Datasets
Laser applications
laser slam
Lasers
lidar
Localization
mobile mapping
Optimization
Optimization algorithms
point clouds
precision optimization
Principal components analysis
Registration
Remote sensing
Segments
Simultaneous localization and mapping
Spatial data
Terrestrial environments
Three dimensional models
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11202352