Fast real-time localization with sparse digital maps for connected automated vehicles in urban areas

Abstract

Abstract For the realization of advanced automation systems in road vehicles, accurate and robust localization is a crucial requirement. Satellite-based systems such as GPS are generally able to provide geolocations, but their precision and robustness can be impaired strongly due to shading of satellites and multipath effects, especially in urban surroundings. Localization methods based on environment perception sensors and digital maps are therefore widely used in the field of autonomous vehicles with accuracy, robustness, real-time capability and sparseness of the maps being major objectives. In this paper, we present a fast, real-time capable implementation of a Monte Carlo Localization scheme which operates on a storage space efficient digital map and is targeted to provide precise localization in urban surroundings at a rate of 50 Hz. For the experimental evaluation, we use our test vehicle’s LiDAR sensor combined with wheel odometry, inertial measurements and a low-cost GPS.