Mapping and positioning of 3D LiDAR SLAM algorithm in mine environment

doi:10.13474/j.cnki.11-2246.2023.0139

Abstract

Abstract: Aiming at the problems of mapping distortion and large cumulative error in mine terrain mapping, a 3D LiDAR-based synchronous positioning and map construction algorithm is studied. Firstly, the internal and external parameters of the LiDAR and IMU are calibrated to solve the problem that maps cannot be built. Then, the inspection robot collects data sets of three sets of scenes (block roads, mine slopes, and mining areas), compares the mainstream algorithms such as ALOAM, LeGO-LOAM, and LIO-SAM, and uses GNSS data as the true trajectory value. The test shows that the multi-sensor fusion algorithm LIO-SAM has better robustness and positioning accuracy in the mine environment, and the absolute position error of the trajectory is reduced by 21.53% and 60.10% compared with ALOAM and LeGO-LOAM, respectively. In addition, the feature extraction part introduces strength features so as to error of the IALIO algorithm is 20.16% lower than that of LIO-SAM.

Key words: mine, SLAM, inspection robot, LiDAR, IMU, feature extraction

CLC Number:

P237

ZHANG Qingyu, CUI Lizhen, DU Xiuduo, MA Baoliang. Mapping and positioning of 3D LiDAR SLAM algorithm in mine environment[J]. Bulletin of Surveying and Mapping, 2023, 0(5): 72-77.

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