Registration Method for Point Clouds of Complex Rock Mass Based on Dual Structure Information

Obtaining complete point cloud data is the basis of rock surface segmentation and related rock-mass numerical simulation. The existing rock-mass point cloud registration methods usually extract point features as the key information for registration. However, the calculation results of point features may be affected by many factors such as data integrity, point density, and noise, which limits the application of existing algorithms in some complex rock scenes (complex collection conditions and complex surface structures). In this article, we propose to divide the rock-mass registration task into two stages: global matching and local matching. During global matching, we extract structure-level features (interrelationships between planes) and shape features (point distribution information in a specific region) instead of point features as the basis for establishing preliminary correspondence between point clouds, achieving robust and efficient region-to-region matching. In the local matching stage, the method based on feature point extraction and matching is proposed to establish accurate point-to-point correspondences in the local region, thus effectively solving the influence of the error of structure-level feature matching on the registration accuracy. In this article, the registration accuracy and efficiency of our method are fully validated by using both repository datasets and real-scene datasets. The robustness of the method is also demonstrated under various conditions. The experimental results show that the root-mean-square error of this method is less than 0.05 m when dealing with mountain data with a length greater than 200 m, which is obviously better than the existing best method.