Volume 50 Issue 10
Oct.  2024
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CUI Shaoyun, BAO Jiusheng, HU Deping, et al. Research status and development trends of SLAM technology in autonomous mining field[J]. Journal of Mine Automation，2024，50（10）：38-52.  doi: 10.13272/j.issn.1671-251x.2024070010
Citation: CUI Shaoyun, BAO Jiusheng, HU Deping, et al. Research status and development trends of SLAM technology in autonomous mining field[J]. Journal of Mine Automation，2024，50（10）：38-52.  doi: 10.13272/j.issn.1671-251x.2024070010
shu

Research status and development trends of SLAM technology in autonomous mining field

doi: 10.13272/j.issn.1671-251x.2024070010
  • 1.

    School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China

  • 2.

    Xuzhou XCMG Heavy Vehicle Co., Ltd., Xuzhou 221112, China

  • 3.

    CCTEG Taiyuan Research Institute, Taiyuan 030006, China

  • Received Date: 2024-07-03
  • Rev Recd Date: 2024-10-28
    • Available Online: 2024-09-29
    Abstract
  • Autonomous driving is identified as one of the key technologies for mining intelligence, with simultaneous localization and mapping (SLAM) technology serving as a key link to realize autonomous driving. To advance the development of SLAM technology in autonomous mining, this paper discusses the principles of SLAM technology, mature ground SLAM solutions, the current research status of mining SLAM, and future development trends. Based on the sensors employed in SLAM technology, the study analyzes the technical principles and corresponding frameworks from three aspects: vision, laser, and multi-sensor fusion. It is noted that visual and laser SLAM technologies, which utilize single cameras or LiDAR, are susceptible to environmental interference and cannot adapt to complex environments. Multi-sensor fusion SLAM emerges as the most effective solution. The research examines the status of mining SLAM technology, analyzing the applicability and research value of visual, laser, and multi-sensor fusion SLAM technologies in underground coal mines and open-pit mines. It concludes that multi-sensor fusion SLAM represents the optimal research approach for underground coal mines, while the research value of SLAM technology in open-pit mines is limited. Based on the challenges identified in underground SLAM technology, such as accumulated errors over time and activity range, adverse effects from various scenes, and the inadequacy of various sensors to meet the hardware requirements for high-precision SLAM algorithms, it is proposed that future developments in SLAM technology for autonomous mining should focus on multi-sensor fusion, solid-state solutions, and intelligent development.

     

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