Localization inside a populated parking garage by using particle filters with a map of the static environment

Abstract

For a vehicle driving safe inside a parking garage autonomously, it is necessary to build a map with its surroundings and also to localize itself within this map. This is known as Simultaneous Localization And Mapping (SLAM). To enable the vehicle to drive autonomously to an assigned parking slot, a parking area, or the exit, the vehicle also needs knowledge about the whole map of the parking garage. This map only contains static elements of the parking garage. Variable elements are not known to the parking garage and therefore are not contained in this static map. In order to reach a target, the vehicle needs to localize itself with respect to this static map. In this contribution the use of such a static map is proposed to support SLAM. This enables SLAM to determine poses related to a static map. Also the performance of SLAM is improved.