Lossless compression of LiDAR frames

LiDARs are sensors which obtain a three-dimensional mapping of the surrounding environment. In addition to provide accurate localization and mapping information, LiDARs can be used in object detection and in autonomous driving. These sensors produce a large amount of data and so it is necessary to implement compression techniques, especially if the data has to be processed in real time, e.g., for safety-critical services. In this thesis two different techniques for LiDAR frame compression were compared and implemented, 2D- and 3D-oriented. The performance had been analyzed in terms of compression efficiency, quality of the decompressed frame compared to the original one and time spent to do the compression. Only lossless compression methods have been examined, limited to intraframe operations. We demonstrated that, thanks to the form in which LiDAR frames are saved, compression methods already known like those for two-dimensional images have given equivalent results, if not better, than those designed for three-dimensional point clouds. This thesis also presented some advanced strategies based on spherical coordinates to improve image-based compression results.