Novel high-performance automatic removal method of interference points for point cloud data in coal mine roadway environment

ABSTRACT

In coal mine roadway, the reconstruction model obtained by 3D laser scanning is interfered by internal point cloud including dust, spray, auxiliary transportation vehicles, personnel flow, and so on, which seriously impacts deformation monitoring based on it. However, most of the current denoising methods barely consider the working conditions of roadway. Thus, this paper developed an automatic removal method of interference points with high efficiency and accuracy in coal mine roadway. It is realized through the establishment and transformation of local coordinate system, construction of voxel grid, bidirectional projection and central axis extraction, and noise removal based on a stepping bounding box. Under different levels of random noise and obstacle point clouds, the method has good performance. Based on the experimental results carried out in an underground roadway, the proposed approach takes 10 s to remove the interference points with an accuracy of 96%, which verifies the feasibility and effectiveness of the method.

KEYWORDS:

• coal mine roadway
• roadway deformation monitoring
• point cloud denoising
• laser scanning
• bounding box

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant [52174153] and Grant [51607178], in part by the Fundamental Research Funds for the Central Universities under Grant [2021YCPY0109] and Grant [2022QN1042], in part by the Natural Science Foundation of Jiangsu Province under Grant [BK20221545] and Grant [BK20221120], in part by the China Postdoctoral Science Foundation under Grant [2021M703509], in part by the Project supported by State Key Laboratory of Precision Measuring Technology and Instruments under Grant [pilab2201], in part by the Funding of Chengdu Guojia Electrical Engineering Co., Ltd. under Grant [NEEC-2022-A06], in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant [21KJB460027]; and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).