Efficient geological point cloud registration method combining dimension reduction and feature points

Abstract

In geological scene registration with laser-scanned point cloud data, traditional algorithms often face reduced precision and efficiency due to extensive data volume and scope, which increase complexity and computational demands. This study introduces, to our knowledge, a novel registration method to address these limitations. Through dimension reduction that integrates height and curvature data, this approach converts point clouds into images, streamlining feature extraction. Log-variance enhancement mitigates information loss from dimensionality reduction, aiding in coarse registration. Further, incorporating weighted distances of feature points into the Iterative Closest Point (ICP) algorithm improves precision in point matching. Experiments indicate an average threefold increase in initial registration efficiency compared to traditional coarse registration algorithms, with improvements in accuracy. The optimized ICP algorithm achieves 50% and 15% accuracy improvements across various datasets, enhancing large-scale geological point cloud data registration.