PROCESSING UAV AND LIDAR POINT CLOUDS IN GRASS GIS
Keywords: 3D rasters, decimation, sampling, binning, LAS, PDAL, PCL, Kinect
Abstract. Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.