Step and curb detection for autonomous vehicles with an algebraic derivative-based approach applied on laser rangefinder data

International audiencePersonal Mobility Vehicles (PMV) is is an important part of the Intelligent Transportation System (ITS) domain. These new transport systems have been designed for urban traffic areas, pedestrian streets, green zones and private parks. In these areas, steps and curbs make the movement of disable or mobility reduced people with PMV, and with standard chair wheels difficult. In this paper, we present a step and curb detection system based on laser sensors. This system is dedicated to vehicles able to cross over steps, for transportation systems, as well as for mobile robots. The system is based on the study of the first derivative of the altitude and highlights the use of a new algebraic derivative method adapted to laser sensor data. The system has been tested on several real scenarios. It provides the distance, altitude and orientation of the steps in front of the vehicle and offers a high level of precision, even with small steps and challenging scenarios such as stairs

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-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

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

Geodetic infrastructure of Serbia

Geodetic reference systems and their realization at the territory of Serbia have been created and maintained since the end of 19th century. Until mid-80s a series of reference geodetic networks were established: trigonometric networks in four orders, two levelling networks of high accuracybut also a series of gravimetric networks. In the following period of 20 years, there were not any organized worksaiming to maintenance of existing networks and creating new ones. In 1996, works started again on developing a new geodetic infrastructure in the form of realizing: a passive geodetic network, a network of permanent stations (AGROS – the active geodetic reference network of Serbia) as well as basic gravimetric networks. In this paperwork, a short review of works aiming to establish and use said networks is given but also a series of suggestions for a future development of geodetic infrastructure of Serbia