Abstract
Accurate self-vehicle localization is an important task for autonomous driving and ADAS. Current GNSS-based solutions do not provide better than 2–3 m in open-sky environments [1]. Moreover, map-based localization using HD maps became an interesting source of information for intelligent vehicles. In this paper, a Map-based localization using a multi-layer LIDAR is proposed. Our method mainly relies on road lane markings and an HD map to achieve lane-level accuracy. At first, road points are segmented by analysing the geometric structure of each returned layer points. Secondly, thanks to LIDAR reflectivity data, road marking points are projected onto a 2D image and then detected using Hough Transform. Detected lane markings are then matched to our HD map using Particle Filter (PF) framework. Experiments are conducted on a Highway-like test track using GPS/INS with RTK correction as ground truth. Our method is capable of providing a lane-level localization with a 22 cm cross-track accuracy.
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