Volume 49 Issue 8
Aug.  2023
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GAO Haiyue, WANG Kai, WANG Baobing, et al. Positioning method for underground unmanned aerial vehicles in coal mines based on global point cloud map[J]. Journal of Mine Automation，2023，49(8)：81-87, 133. DOI: 10.13272/j.issn.1671-251x.2022110024
Citation: GAO Haiyue, WANG Kai, WANG Baobing, et al. Positioning method for underground unmanned aerial vehicles in coal mines based on global point cloud map[J]. Journal of Mine Automation，2023，49(8)：81-87, 133. DOI: 10.13272/j.issn.1671-251x.2022110024
shu

Positioning method for underground unmanned aerial vehicles in coal mines based on global point cloud map

  • CCTEG Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China

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  • Received Date: November 06, 2022
  • Revised Date: August 15, 2023
  • Available Online: September 03, 2023
    Graphical Abstract
    • Abstract
  • When simultaneous localization and mapping (SLAM) technology is applied to autonomous positioning of unmanned aerial vehicles in coal mines, the use of feature points to construct maps can easily lead to degradation issues, resulting in inaccurate positioning. Moreover, due to its use of the body as a reference coordinate system, global positioning cannot be achieved. In order to solve the problems, a positioning method for underground unmanned aerial vehicles (UAV) in coal mines based on global point cloud map is proposed. The method uses Fast-LIO2 algorithm as the lidar SLAM algorithm to obtain UAV position and attitude estimation. An iterative nearest-neighbor algorithm is used for two-step matching of the acquired real-time lidar point cloud and the global point cloud map to achieve UAV position and attitude correction. To address the issue of point cloud matching speed not ensuring real-time positioning due to the excessive number of point clouds, a time-based position and attitude output strategy is designed to increase the frequency of outputting UAV position and attitude data. The SLAM precision and position and attitude correction effect of the UAV positioning method are tested in a 1 000 m underground coal mine roadway. The results show that in long-distance roadway environments, the cumulative positioning error of the Fast-LIO2 algorithm is less than 1 m, and is less than 0.3 m in the range of 600 m or more, which is significantly smaller than the cumulative positioning errors of LOAM-Livox algorithm and LIO-Livox algorithm. The position and attitude estimation output by the Fast-LIO2 algorithm has been corrected by the correction algorithm, and all flight paths are located in the global point cloud map, verifying the effectiveness of the position and attitude correction algorithm. The time consumption of single SLAM algorithm operation is 14.83 ms, the one of single position and attitude correction is 883 ms, and the output frequency of position and attitude data is 10 Hz, meeting the real-time requirements of UAV positioning.
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