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 multilayer 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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Accurate localization is very important to ensure performance and safety of autonomous vehicles. In particular, with the appearance of High Definition (HD) sparse geometric road maps, many research works have been focusing on the deployment of accurate localization systems in a previously built map. In this paper, we solve a localization problem by matching road perceptions from a 3D LIDAR sensor with HD map elements. The perception system detects High Reflective Landmarks (HRL) such as: lane markings, road signs and guard rail reflectors (GRR) from a 3D point cloud. A particle filtering algorithm estimates the position of the vehicle by matching observed HRLs with HD map attributes. The proposed approach extends our work in [1] and [2] where a localization system based on lane markings and road signs has been developed. Experiments have been conducted on a highway-like test track using GNSS/INS with RTK corrections as a ground truth (GT). Error evaluations are given as cross-track (CT) and along-track (AT) errors defined in the curvilinear coordinates [3] related to the map. The obtained accuracies of our localization system is 18 cm for the crosstrack error and 32 cm for the along-track error.