Homogenization research of waveform sampling LiDAR point cloud data

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.