Segmentation, Classification and Tracking of Objects in LiDAR Point Cloud Data Using Deep Learning
The purpose of this thesis was to explore deep learning methods of segmentation, classification and tracking of objects in LiDAR data. To do this a complete pipeline was developed, consisting of background filtering, clustering, tracking, labeling and visualization. The objects that were focused on were pedestrians, cyclists, cars and animals, in different environments. Background segmentation and
