PT Unknown AU Haase, K TI Maritime Augmented Reality with a prioriknowledge of sea charts PY 2013 PU Christian-Albrechts-Universität zu Kiel WP https://macau.uni-kiel.de/receive/diss_mods_00011787 LA en DE Augmented Reality; Topology S-57; ENC; Object Detection; Bayesian Network; Topologie S-57; Objekterkennung; Bayes'sche Netzwerke AB The main objective of this thesis is to provide a concept to augment mar- itime sea chart information into the camera view of the user. The benefit is the simpler navigation due to the offered 3D information and the overlay onto the real 3D environment. In the maritime context special conditions hold. The sensor technologies have to be reliable in the environment of a ship’s ferrous construction. The aug- mentation of the objects has to be very precise due to the far distances of observable objects on the sea surface. Furthermore, the approach has to be reliable due to the wide range of light conditions. For a practical solution, the system has to be mobile, light-weight and with a real-time performance. To achieve this goal, the requirements are set, the possible measurement units and the data base structure are presented. First, the requirements are analyzed and a suitable system is designed. By the combination of proper sensor techniques, the local position and orienta- tion of the user can be estimated. To verify the concept, several prototypes with exchangeable units have been evaluated. This first concept is based on a marker-based approach which leads to some drawbacks. To overcome the drawbacks, the second aspect is the improvement of the sys- tem and the analysis of markerless approaches. One possible strategy will be presented. The approach uses the statistical technique of Bayesian networks to vote for single objects in the environment. By this procedure it will be shown, that due to the a priori information the underlying sea chart system has the most benefit. The analysis of the markerless approach shows, that the sea charts structure has to be adapted to the new requirements of interactive 3D augmentation scenes. After the analysis of the chart data concept, an approach for the optimization of the charts by building up an object-to-object topology within the charts data and the Bayesian object detection approach is presented. Finally, several evaluations show the performance of the imple- mented evaluation application. PI Kiel ER