Abstract: Existing dynamic point cloud removal methods are mostly designed for structured scenarios. When applied to unstructured coal mine environments characterized by non-uniform point cloud distribution, large spatial structural differences, and large-scale dynamic occlusions, these methods tend to cause ground truncation and local holes in the global map, thereby damaging map continuity. To address these issues, an adaptive cascaded dynamic point cloud removal method for unstructured coal mine scenarios was proposed. The proposed method first converted three-dimensional point clouds into the two-dimensional image domain using multi-resolution depth projection and rapidly generated prior labels of dynamic regions by constructing depth consistency constraints; then, the space was discretized using a ring–sector strategy, and the decision threshold of the Scan Ratio Test (SRT) was adaptively adjusted based on the prior information of dynamic regions to determine whether dynamic point clouds existed within each grid cell; subsequently, secondary verification was performed on grid cells containing dynamic point clouds by incorporating a height lower-bound check, and Regional Ground Plane Fitting (R-GPF) was applied to recover ground point clouds missing due to dynamic occlusion; finally, an accurate and continuous static point cloud map was obtained. Experimental results on public datasets and simulated coal mine scenarios showed that the proposed method achieved good mapping consistency, effectively removed dynamic point clouds while preserving the integrity of vertical geometric structures and the continuity of ground surfaces in dynamic neighborhoods, effectively suppressed false ghosting caused by mapping pose drift, avoided ground holes and wall fractures, and maintained the continuity of static point cloud structures. The Static Accuracy (SA) of the proposed method was higher than that of the ERASOR method, the Dynamic Accuracy (DA) was higher than that of the Removert method, and both the Average Accuracy (AA) and Harmonic Accuracy (HA) were optimal, effectively balancing dynamic point removal accuracy and static point fidelity.