New Approaches for Estimating the Local Point Density and its Impact on Lidar Data Segmentation

Lidar systems have been proven as a cost-effective tool for the collection of high density and accurate point cloud over physical surfaces. The collected point cloud does not exhibit homogenous point distribution due to the characteristics of the scanning system and/or the physical properties of the scanned surfaces. In order to effectively process the lidar point clouds, local point density variations should be quantified and taken into account for the definition of processing parameters. In this paper, new approaches are presented for the estimation of local point density indices while considering the 3D relationship among lidar points, the physical properties of the reflecting surfaces, and the noise level in the datasets collected by different laser scanners. The impact of considering the estimated local point density variations on the quality of lidar data segmentation results is then investigated by performing a quality control procedure. Quantitative evaluation of segmentation results highlights the efficacy of utilizing the estimated local point density indices for the derivation of more accurate segmentation.