An improved method of point cloud filtering based on convex hull algorithm for vertical shaft

As the key channel in the operation of coal mine, the structural stability of the shaft is directly related to the safety production of mine. In order to improve the accuracy and efficiency of wellbore deformation monitoring, 3D laser scanning system, as a new technology, is gradually applied to wellbore deformation monitoring. Due to the existence of lifting equipment, pipelines, cables and other auxiliary facilities inside the wellbore, the application of automatic 3D laser scanning data is often affected by noise interference and occlusion, resulting in the traditional filtering method facing great challenges in accuracy and robustness when extracting wellbore wall point cloud. In order to improve the accuracy and robustness of wellbore deformation monitoring data, this study improves the quick hull algorithm and constructs an efficient filtering method for ancillary facilities by introducing an adaptive distance threshold judgment mechanism. This method first slices the wellbore point cloud data and then extracts the wellbore boundary by using quick hull. Instead, it automatically calculates and adjusts an appropriate threshold according to the local characteristics of the currently processed data (point cloud density, distribution) to effectively separate the wellbore and ancillary facilities point cloud. Experimental results show that, compared with the circular fitting filtering method based on random sampling consensus algorithm (RANSAC), the proposed method has significant advantages in terms of operation efficiency, and the processing speed is more than doubled. At the same time, in terms of fitting accuracy, the standard deviation of the results obtained by this method is reduced by 16.4% compared with RANSAC method, which shows that it can more effectively retain the real wellbore structure data, and significantly suppress the interference noise introduced by ancillary facilities and other factors, thus reflecting better filtering performance and reliability.