Night-vision stereo grid mapping for digital map localization

Abstract

Accurate and robust environment perception is a prerequisite for advanced driver assistance systems such as parking assistance, collision avoidance or night vision systems but also for robot navigation. In this context, occupancy grid mapping is a common way to represent the static car surroundings perceived by radar, laser or camera sensors. This work focuses on the challenges of nighttime grid mapping with a stereo camera — disparity images are perturbed by severe noise caused by wrong correspondences and headlights of oncoming vehicles. Noise removal and headlight detection based on the input images are proposed to make the disparity image suitable for a 2.5D occupancy grid mapping algorithm. In the second part of the work the concrete application of such a stereo grid map is shown and evaluated: The stereo map is applied to a localization task on a digital navigation map. The vehicle's position and orientation to the given navigation map are estimated in a particle filter system where the stereo grid map serves as measurement. Evaluations on real-world sequences and comparisons with an existing radar-based positioning system classify the approach's performance.