RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway

Abstract

With the rapid development of the geographic information service industry, point cloud data are widely used in various fields, such as architecture, planning, cultural relics protection, mining engineering, etc. Despite that there are many approaches to collecting point clouds, we are facing the problem of point cloud holes caused by the inability of a 3D laser scanner to collect data completely in the narrow space of the mine access shaft. Thus, this paper uses RGB-D cameras to collect data and reconstruct the hole in the point cloud. We used a 3D laser scanner and RGB-D depth camera to collect the 3D point cloud data of the access shaft roadway. The maximum error was 2.617 cm and the minimum error was 0.031 cm by measuring the distance between the feature points, which satisfied the visualization repair of the missing parts of the 3D laser scanner data collection. We used the FPTH + ICP algorithm, ISS + ICP algorithm, SVD + ICP algorithm, and 3D-NDT algorithm to perform registration and fusion of the processed 3D point cloud and the original point cloud and finally repaired the hole. The study results show that the ISS + ICP registration algorithm had the most matching points and the lowest RMSE value of 13.8524 mm. In addition, in the closed and narrow roadway, the RGB-D camera was light and easy to operate and the point data acquired by it had relatively high precision. The three-dimensional point cloud of the repaired access shaft roadway has a good fit and can meet the repair requirements.

Research on point cloud hole filling and 3D reconstruction in reflective area

“…After refinement and adjustment, the point cloud can be smoothly transitioned 12 . A mesh deformation-based approach to digitized hole filling is proposed that relies on a priori knowledge of the numerical model as a nominal mesh and performs the deformation of the nominal mesh after identifying the digitized holes and calculating the difference between the nominal mesh and the point cloud, which is effectively verified on industrial parts 13 .A method to repair point cloud holes by combining FPFH with ICP algorithm for registering and fusing the processed point cloud model with the original point cloud 14 . The method can also perform local repair by dividing the point cloud holes into small patches based on feature lines, and then using NURBS to fit surfaces to gradually achieve repair of the patches and complete the global repair 15 .…”

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

“…Meanwhile, [184] employed a Kinect depth camera for image capture, accompanied by an improved iterative closest point algorithm, facilitating 3D reconstruction in mining contexts. Additionally, [185] collected mining data and reconstructed point clouds using RGB-D cameras to repair the holes in the original point clouds obtained from 3D laser scanner. Moreover, [186] applied machine vision reconstruction methods to underground patrol robots to monitor deformation in coal mine tunnels.…”

Research on point cloud hole filling and 3D reconstruction in reflective area

“…After refinement and adjustment, the point cloud can be smoothly transitioned [7]. A mesh deformation-based approach to digitized hole filling is proposed that relies on a priori knowledge of the numerical model as a nominal mesh and performs the deformation of the nominal mesh after identifying the digitized holes and calculating the difference between the nominal mesh and the point cloud, which is effectively verified on industrial parts [8].A method to repair point cloud holes by combining FPFH with ICP algorithm for registering and fusing the processed point cloud model with the original point cloud [9]. The method can also perform local repair by dividing the point cloud holes into small patches based on feature lines, and then using NURBS to fit surfaces to gradually achieve repair of the patches and complete the global repair [10].…”