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

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 Lane Marking Detection For Vehicle Positioning in an HD Map

International audienceAccurate self-vehicle localization is an important task for autonomous driving and ADAS. Current GNSS-basedsolutions do not provide better than 2-3 m in open-sky environments. Moreover, map-based localization using HDmaps 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 toLIDAR 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 aHighway-like test track using GPS/INS with RTK correction as ground truth. Our method is capable of providing a lane-levellocalization with a 22 cm cross-track accuracy

Localisation précise d’un véhicule autonome en utilisant des télémètres lasers et une carte précise de l’environnement sur autoroutes

In this thesis, we address the problem of accurate localization of autonomous vehicles on highway roads using LiDAR sensors and a highly accurate third party map. The proposed approach is based on two core modules: perception and map-matching. The perception module uses the 3D data enhanced by the LiDAR reflectivity to detect road primitive features: lane markings, barriers, traffic signs and guardrail reflectors. The map-matching module incorporates these measurements and aligns them against a highly accurate third party map. The map-matching is performed using a particle filter, which we have improved in order to deal with the particle deprivation problem. The proposed improvement uses the road geometry in order to optimize the spatial distribution of particles while maintaining the number of particles constant. To evaluate the proposed method, we compared the localization outputs of our system to a Global Navigation Satellite System (GNSS) with RTK corrections (ground truth). Experiments have been conducted on two highway roads. The first is an experimental test track (CTA2) of 5 km long located at CTA, Renault’s Aubevoye’s Technical Center. This track is designed to exactly replicate a two-lane highway environment. The second is a section of the A13 highway, running from Paris and ending at Aubevoye. The results are promising and show the feasibility of a localization system based on LiDARs alone and with a sparse map data representation.Dans le cadre de cette thèse, un système de perception à base d’un capteur LIDAR et un système de localisation sur une carte numérique très précise ont été développés dans le contexte des développements des véhicules autonomes. Le système de perception proposé utilise les données 3D augmentées par la réflectivité du LiDAR afin de détecter les marquages au sol, les barrières, les panneaux de signalisation et les rétro-reflecteurs placés sur les barrières ou rails de sécurité dans un environnement autoroutier. Les objets détectés sont ensuite recalés par rapport à une carte numérique très précise. Cette dernière contient les lignes de marquage dans un format spécifique, les panneaux de signalisation ainsi que d’autres attributs sémantiques. Le recalage est assuré via une implémentation d’un filtre particulaire auquel nous avons effectué des améliorations pour optimiser la distribution des particules sans pour autant en modifier le nombre. Cette méthode est appelée: la mise à jour contrainte du filtre. Pour évaluer la méthode proposée, nous avons utilisé un système de navigation satellitaire (GNSS) avec correction RTK comme une vérité terrain et nous avons adopté différentes métriques pour montrer la précision de notre système. Les expériences ont été menées sur deux autoroutes: une piste de test propre à Renault et un tronçon d’environ 50 kilomètres sur route ouverte. Les résultats sont prometteurs et montrent la faisabilité d’un système de localisation fondé sur des LiDARs seuls et avec une représentation éparse des données (sous forme d’amers plutôt que la totalité du nuage de points)

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

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