Homogenization research of waveform sampling LiDAR point cloud data

Zhiwei Dong; Liyu Sun; Shen Tan; Tao Xu; Runsu Gao; Deying Chen

doi:10.1117/12.2502311

12 December 2018 Homogenization research of waveform sampling LiDAR point cloud data

Zhiwei Dong, Liyu Sun, Shen Tan, Tao Xu, Runsu Gao, Deying Chen

Proceedings Volume 10845, Three-Dimensional Image Acquisition and Display Technology and Applications; 1084502 (2018) https://doi.org/10.1117/12.2502311
Event: International Symposium on Optoelectronic Technology and Application 2018, 2018, Beijing, China

Abstract

The streak tube imaging LiDAR has promising application prospect due to the ability of full waveform sampling and high sensitivity. This kind of LiDAR generates massive point cloud data with high efficiency. However, the distribution of laser foot points is found usually irregular due to the scanning mode of this kind of LiDAR. This paper focuses on the interpolation of ideal points to realize uniform distribution of the point cloud using interpolation techniques, including nearest neighbor, arithmetic mean and inverse distance weighted interpolation. Specifically, we propose a new homogenization method in which the inverse distance weighted interpolation is improved. The suitability of homogenization methods for point cloud generated by streak tube imaging LiDAR is tested. The results show that the nearest neighbor method has better restoration of buildings with abrupt elevation values and inverse distance weighted interpolation outperforms other selected methods when processing flatland. It has been proven that the new method we propose possesses the advantages of both nearest neighbor and inverse distance weighted techniques.

Citation Download Citation

Zhiwei Dong, Liyu Sun, Shen Tan, Tao Xu, Runsu Gao, and Deying Chen "Homogenization research of waveform sampling LiDAR point cloud data", Proc. SPIE 10845, Three-Dimensional Image Acquisition and Display Technology and Applications, 1084502 (12 December 2018); https://doi.org/10.1117/12.2502311

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