Robust LiDAR point cloud registration in degraded environments via deep target classification and distributed centre localisation

Accurate registration of light detection and ranging (LiDAR) point clouds is essential for underground measurement, monitoring, and structural assessment. However, conventional registration methods often fail in degraded underground environments. This is mainly because geometric features are sparse and tunnel surfaces provide limited texture. To address this challenge, we propose a measurement-oriented registration framework that integrates coarse-to-fine target extraction strategy with distributed centre localisation. First, we leverage reflectivity priors to rapidly identify candidate planar targets from raw LiDAR scans. Next, a lightweight PointNet++ variant (IS-PointNet++) performs fine-grained classification to distinguish artificial planar targets from noise and clutter. We then introduce a distributed localisation strategy that uses stochastic overlapping-interval sampling and ensemble aggregation of local estimates to determine target centres robustly. This design mitigates localisation errors caused by incomplete data and partial occlusions. Finally, the transformation is estimated from a minimal set of four corresponding points. Experiments in tunnels and coal-mine roadways showed that the proposed framework consistently yielded lower registration errors than widely used baselines under different overlap ratios. These results highlight the robustness and practical utility of the proposed framework for reliable underground measurement and long-term monitoring.