A point cloud denoising method for unstructured roadways based on regional growth

Currently, research on point cloud denoising in underground roadways has not fully met the special denoising needs of roadway point clouds. Especially in narrow, enclosed, and complex underground roadway environments, the research has not fully addressed the challenges caused by pipe wall attachment...

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Bibliographic Details
Published inGong kuang zi dong hua = Industry and mine automation Vol. 50; no. 3; pp. 48 - 55
Main Authors LIAN Zhongwen, REN Zhuli, HAO Yinghao, YANG Fan, BAI Gang, FANG Cheng, YUAN Ruifu
Format Journal Article
LanguageChinese
Published Editorial Department of Industry and Mine Automation 01.03.2024
Subjects
3d laser scanning
kd-tree
point cloud denoising
point cloud region segmentation
regional growth
unstructured roadway
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Summary:Currently, research on point cloud denoising in underground roadways has not fully met the special denoising needs of roadway point clouds. Especially in narrow, enclosed, and complex underground roadway environments, the research has not fully addressed the challenges caused by pipe wall attachments, dust, and human noise. By analyzing the unstructured scenes and sensor errors underground, considering the noise caused by personnel, mobile devices, and pipeline networks, a point cloud denoising method for unstructured roadways based on region growth is proposed. The method uses 3D laser scanning technology to obtain 3D point cloud information of underground roadway scenes, and analyzes the abnormal points caused by unstructured underground scenes and sensor errors, as well as the noise features formed by personnel, mobile devices, and air and water pipelines. The method uses k-dimensional trees (kd-tree) to construct the topological relationship of point clouds, selects appropriate seed nodes and growth crite
ISSN:1671-251X
DOI:10.13272/j.issn.1671-251x.2024010037