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

LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic

This paper presents a system for improving the robustness of LiDAR lateral localisation systems. This is made possible by including detections of road boundaries which are invisible to the sensor (due to occlusion, e.g. traffic) but can be located by our Occluded Road Boundary Inference Deep Neural Network. We show an example application in which fusion of a camera stream is used to initialise the lateral localisation. We demonstrate over four driven forays through central Oxford - totalling 40 km of driving - a gain in performance that inferring of occluded road boundaries brings.Comment: accepted for publication at the IEEE/ION Position, Location and Navigation Symposium (PLANS) 202

Road Markings Segmentation from LIDAR Point Clouds using Reflectivity Information

Lane detection algorithms are crucial for the development of autonomous vehicles technologies. The more extended approach is to use cameras as sensors. However, LIDAR sensors can cope with weather and light conditions that cameras can not. In this paper, we introduce a method to extract road markings from the reflectivity data of a 64-layers LIDAR sensor. First, a plane segmentation method along with region grow clustering was used to extract the road plane. Then we applied an adaptive thresholding based on Otsu s method and finally, we fitted line models to filter out the remaining outliers. The algorithm was tested on a test track at 60km/h and a highway at 100km/h. Results showed the algorithm was reliable and precise. There was a clear improvement when using reflectivity data in comparison to the use of the raw intensity data both of them provided by the LIDAR sensor

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