Generation of an integrated 3D city model with visual landmarks for autonomous navigation in dense urban areas

In the context of urban autonomous navigation systems for going from point A to point B, a practicable trajectory which takes into account drivable areas and permanent obstacles should be designed first. A robot should then follow this trajectory while avoiding not only dynamic obstacles such as cars and pedestrians but also permanent obstacles such as road sides and central islands. To this end, a robot must be aware of its exact position and must be informed of what its immediate environment is at all times. In dense urban areas, GNSS systems generally suffer from lack of precision due to masks and multipaths. Localization systems have to model these phenomena and even merge with vision based methods in order to obtain the high accuracies required in the process. In this paper we propose an integrated geographic database enabling, on the one hand, the GNSS and vision based localization methods to obtain the required accuracy, and on the other, to provide the robots with information about its surroundings such as drivable surfaces and permanent obstacles. The database is comprised of 3D buildings, 3D roads and a set of 3D visual landmarks. Our system provides most of the information required for autonomous navigation in dense urban areas and has successfully been embedded in real experiments, thanks to a real-time querying system.