LIDAR-Based High Reflective Landmarks (HRL)s For Vehicle Localization in an HD Map

International audienceAccurate 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 cross-track error and 32 cm for the along-track error

LIDAR-Based road signs detection For Vehicle Localization in an HD Map

International audienceSelf-vehicle localization is one of the fundamental tasks for autonomous driving. Most of current techniques for global positioning are based on the use of GNSS (Global Navigation Satellite Systems). However, these solutions do not provide a localization accuracy that is better than 2-3 m in open sky environments [1]. Alternatively, the use of maps has been widely investigated for localization since maps can be pre-built very accurately. State of the art approaches often use dense maps or feature maps for localization. In this paper, we propose a road sign perception system for vehicle localization within a third party map. This is challenging since third party maps are usually provided with sparse geometric features which make the localization task more difficult in comparison to dense maps. The proposed approach extends the work in [2] where a localization system based on lane markings has been developed. Experiments have been conducted on a Highway-like test track using GNSS/INS with RTK corrections as ground truth (GT). Error evaluations are given as cross-track and along-track errors defined in the curvilinear coordinates [3] related to the map

Using a single band GNSS receiver to improve relative positioning in autonomous cars

We show how the combination of a single band global navigation satellite systems (GNSS) receiver, standard automotive level inertial measurement unit (IMU), and wheel speed sensors, can be used for relative positioning with accuracy on a decimeter scale. It is realized without the need for expensive dual band receivers, base stations or long initialization times. This is implemented and evaluated in a natural driving environment against a reference systems and against two simple base line systems; one using only IMU and wheel speed sensors, the other also adding basic GNSS. The proposed solution provides substantially slower error growth than either of the two base line systems