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MU Qi, LIANG Xin, GUO Yuanjie, WANG Yuhao, LI Zhanli. An edge aware enhanced visual SLAM method for underground coal mines[J]. COAL GEOLOGY & EXPLORATION.
Citation: MU Qi, LIANG Xin, GUO Yuanjie, WANG Yuhao, LI Zhanli. An edge aware enhanced visual SLAM method for underground coal mines[J]. COAL GEOLOGY & EXPLORATION.
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An edge aware enhanced visual SLAM method for underground coal mines

  • 1. College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China;

  • 2. College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

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  • Received Date: August 22, 2024
  • Revised Date: February 04, 2025
    Graphical Abstract
    • Abstract
  • [Objective] Low illumination, weak textures, and degraded structured features are commonly found in underground coal mines, resulting in insufficient effective features or high mismatch rates in Visual SLAM (Simultaneous Localization and Mapping) systems. This severely limits the accuracy and robustness of localization. [Methods] An edge aware enhancement based Visual SLAM method is proposed. Initially, an edge aware constrained low light image enhancement module is constructed. The Retinex algorithm is optimized with an adaptive scale gradient domain guided filter to obtain images with clear textures and uniform illumination, sensibly improving feature extraction performance under low and uneven lighting conditions. Subsequently, an edge aware enhanced feature extraction and matching module is built in the visual odometry. It enhances feature detectability and matching accuracy in weakly textured and structured environments. The point and line features are extracted using ORB(Oriented FAST and Rotated BRIEF) and EDLines(Edge Drawing Lines) algorithms, with precise matching achieved through GMS(Grid-based Motion Statistics) and ratio test strategies. Finally, the method is evaluated on the TUM dataset and an underground coal mine real world dataset, in comparison with ORB-SLAM2 and ORB-SLAm3, covering image enhancement, feature matching, and localization. [Results and Conclusions] The results show that (1) on the TUM dataset, the proposed method reduces the root mean square error of absolute and relative trajectory errors by 4%~38.46% and 8.62%~50% compared to ORB-SLAM2, and by 0%~61.68% and 3.63%~47.05% compared to ORB-SLAm3, respectively; (2) in underground coal mine real world dataset experiments, the localization trajectory of the proposed method is closer to the camera motion reference trajectory; (3) the proposed method effectively improves the accuracy and robustness of Visual SLAM in feature degradation scenes in underground coal mines, providing a technical solution for the application of Visual SLAM technology in coal mines. Research on Visual SLAM methods for degraded feature scenarios in underground environments is important for advancing the robotization of mobile equipment in coal mines.
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