Semantic Stixels fusing LIDAR for Scene Perception

Abstract: Autonomous driving is the concept of a vehicle that operates in traffic without instructions from a driver. A major challenge for such a system is to provide a comprehensive, accurate and compact scene model based on information from sensors. For such a model to be comprehensive it must provide 3D position and semantics on relevant surroundings to enable a safe traffic behavior. Such a model creates a foundation for autonomous driving to make substantiated driving decisions. The model must be compact to enable efficient processing, allowing driving decisions to be made in real time. In this thesis rectangular objects (The Stixelworld) are used to represent the surroundings of a vehicle and provide a scene model. LIDAR and semantic segmentation are fused in the computation of these rectangles. This method indicates that a dense and compact scene model can be provided also from sparse LIDAR data by use of semantic segmentation.