This master's thesis focuses on representation of geometric information about the surrounding environment for robotic systems. Instead of a common point-cloud based representation, an environment model that stores information about objects described by bounding boxes and planes is proposed. These objects can be obtained from trained object detectors, planar surface detectors, etc. processing data from sensors (e.g. RGB-D camera). The information about the objects (position, etc.) stored in the model is constantly changing according to data obtained from detectors. The environment model is implemented as a module for Robot Operating System. To illustrate and visualize features of the model, a demonstration application was also prepared.

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. / Fullt självkörande fordon behöver inte förare. Ett sådant fordon behöver en precis, detaljerad och kompakt modell av omgivningen baserad på sensordata. Med detaljerad avses att modellen innefattar all information nödvändig för ett trafiksäkert beteende. Med kompakt avses att en snabb bearbetning kan göras av modellen så att fordonet i realtid kan fatta beslut och manövrera i trafiken. I denna uppsats tillämpas en metod där man med rektangulära objekt skapar en modell av omgivningen. Dessa beräknas från LIDAR och semantisk segmentering. Arbetet indikerar att med hjälp av semantisk segmentering kan en tät, detaljerad och kompakt modell göras även från glesa LIDAR-data.