A semantic approach to sensor-independent vehicle localization

As intelligent vehicles become more and more capable, they must learn to navigate and localize themselves in a wide variety of environments, including GPS-denied and only crudely mapped areas. We argue that since autonomous vehicles must be able to perceive, and semantically interpret, their immediate environment, they should be able to use abstract semantic information as their sole means of localization. This simplifies the level of detail and precision required from environment maps so that, for example, a rough floor plan of a parking garage will suffice to autonomously navigate it. We propose a concept for semantic localization which only requires a conceptual semantic map of the environment, and can be made to work with any kind of sensor data from which the required semantic information can be extracted. We present a localization algorithm which may be used as a base for semantic navigation, e.g. in context of automated driving, and some initial results of its application in a parking garage scenario.