A LiDAR SLAM Algorithm Considering Dynamic Extraction of Feature Points in Underground Coal Mine

Abstract

Abstract. To address the absence of GNSS signals in underground coal mines and the susceptibility of mainstream LiDAR SLAM to degradation due to insufficient feature constraints, this paper proposes a tightly-coupled SLAM algorithm incorporating LiDAR and IMU for use in such environments. The paper initially introduces a dynamic feature point extraction strategy, where the number of corner feature points can be dynamically adjusted based on the occurrence of degradation in the underground coal mine environment. This approach constructs a constraint matrix with rich and robust feature information, enhancing pose estimation accuracy in environments with inadequate feature constraints, mitigating degradation effects, and reducing global cumulative error. Subsequently, the consistent building of the underground coal mine environment is achieved through back-end factor graph optimization. Finally, to validate the effectiveness of the method, experimental analysis is conducted in an underground coal mine. The results demonstrate that LeGO_LOAM exhibits poor robustness in underground coal mines and fails to construct a globally consistent pose estimation and map. Conversely, the pose estimation error of the proposed method in this paper is 50.93% lower in the plane direction and 42.13% lower in the elevation direction compared to LIO_SAM. This underscores the method's significance as crucial technical support for the intelligent perception and positioning of robots in underground coal mines.