Single photon point cloud denoising method based on density and local statistics

doi:10.7523/j.ucas.2022.058

Abstract

Abstract: In this paper, aiming at a 64-channel airborne single-photon LiDAR system developed by Beijing Research Institute of Telemetry, a two-dimensional profile point cloud denoising method based on density and local statistics was proposed. First, the elevation range of the point cloud was determined; then a modified DBSCAN algorithm was utilized for coarse denoising; finally, the statistical outlier removal algorithm was adapted for fine denoising and the valid signal point cloud was obtained. The experimental result shows that the method proposed in this paper can adapt to different surface types, the root mean square error of elevation is about 0.27 m, and the accuracy is 90.87%, which is better than the conventional point cloud denoising methods, and can meet the technical requirements of domestic airborne single-photon LiDAR to obtain high-precision three-dimensional surface contours.

Key words: single photon LiDAR, point cloud denoising, local statistics, density clustering

CLC Number:

P237

PAN Chao, LI Lianghai, CAO Haiyi, ZHAO Yiming, YUAN Yifei, HAN Xiaoshuang. Single photon point cloud denoising method based on density and local statistics[J]. Journal of University of Chinese Academy of Sciences, 2024, 41(2): 268-274.

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