Voxel-Based Roadway Terrain Risk Modeling and Traversability Assessment in Underground Coal Mines

Effective roadway environment sensing is critical for intelligent underground vehicle navigation. Dust pollution and complex terrain in underground roadways present key challenges for quantifying passability risks: (1) Over-filtering of dust noise in lidar point clouds can inadvertently remove valuable information. (2) The enclosed and chaotic nature of underground roadways prevents planar information from fully representing spatial constraints. To address these challenges, this paper proposes a method for constructing terrain risk voxels and assessing navigability in coal mine tunnels. First, an improved particle filter combined with image features performs two-stage dust filtering. Second, D-S theory is applied to fuse and evaluate three-dimensional tunnel risks, constructing 3D terrain risk voxels. Finally, navigable spaces are identified and their characteristics quantified to assess passage risks. Experiments show that the proposed dust filtering algorithm achieves 96.7% average accuracy in primary underground areas. The D-S theory effectively constructs roadway terrain risk voxels, enabling reliable quantitative assessment of roadway passability risks.