Improved dynamic object detection within evidential grids framework

International audienceThe deployment of autonomous robots/vehicles is increasing in several domains. To perform tasks properly, a robot must have a good perception about its environment while detecting dynamic obstacles. Recently, evidential grids have attracted more interest for environment perception since they permit more effective uncertainty handling. The latest studies on evidential grids relied on the use of thresholds for information management e.g. the use of a threshold, for the conflict characterized by the mass of empty set, in order to detect dynamic objects. Nevertheless, the mass of empty set alone is not consistent in some cases. Also, the thresholds used were chosen either arbitrary or tuned manually without any computational method. In this paper, first the conflict is composed of two parameters instead of mass of empty set alone, and dynamic objects detection is performed using a threshold on the evolution of this conflict pair. Secondly, the paper introduces a general threshold along with a mathematical demonstration to compute it which can be used in different dynamic object detection cases. A real-time experiment is performed using the RB1-BASE robot equipped with a RGB-D camera and a laser scanner

Semantic evidential grid mapping using monocular and stereo cameras

Accurately estimating the current state of local traffic scenes is one of the key problems in the development of software components for automated vehicles. In addition to details on free space and drivability, static and dynamic traffic participants and information on the semantics may also be included in the desired representation. Multi-layer grid maps allow the inclusion of all of this information in a common representation. However, most existing grid mapping approaches only process range sensor measurements such as Lidar and Radar and solely model occupancy without semantic states. In order to add sensor redundancy and diversity, it is desired to add vision-based sensor setups in a common grid map representation. In this work, we present a semantic evidential grid mapping pipeline, including estimates for eight semantic classes, that is designed for straightforward fusion with range sensor data. Unlike other publications, our representation explicitly models uncertainties in the evidential model. We present results of our grid mapping pipeline based on a monocular vision setup and a stereo vision setup. Our mapping results are accurate and dense mapping due to the incorporation of a disparity- or depth-based ground surface estimation in the inverse perspective mapping. We conclude this paper by providing a detailed quantitative evaluation based on real traffic scenarios in the KITTI odometry benchmark dataset and demonstrating the advantages compared to other semantic grid mapping approaches

Advances in the Bayesian Occupancy Filter framework using robust motion detection technique for dynamic environment monitoring

International audienceThe Bayesian Occupancy Filter provides a framework for grid-based monitoring of the dynamic environment. It allows to estimate dynamic grids, containing both information of occupancy and velocity. Clustering such grids then provides detection of the objects in the observed scene. In this paper we present recent improvements in this framework. First, multiple layers from a laser scanner are fused using opinion pool, to deal with conflicting information. Then a fast motion detection technique based on laser data and odometer/IMU information is used to separate the dynamic environment from the static one. This technique instead of performing a complete SLAM (Simultaneous Localization and Mapping) solution, is based on transferring occupancy information between consecutive data grids, the objective is to avoid false positives (static objects) like other DATMO approaches. Finally, we show the integration with Bayesian Occupancy Filter (BOF) and with the subsequent tracking module called Fast Clustering-Tracking Algorithm (FCTA). We especially show the improvements achieved in tracking results after this integration, for an intelligent vehicle application

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system

Fusion de données multi capteurs pour la détection et le suivi d'objets mobiles à partir d'un véhicule autonome

La perception est un point clé pour le fonctionnement d'un véhicule autonome ou même pour un véhicule fournissant des fonctions d'assistance. Un véhicule observe le monde externe à l'aide de capteurs et construit un modèle interne de l'environnement extérieur. Il met à jour en continu ce modèle de l'environnement en utilisant les dernières données des capteurs. Dans ce cadre, la perception peut être divisée en deux étapes : la première partie, appelée SLAM (Simultaneous Localization And Mapping) s'intéresse à la construction d'une carte de l'environnement extérieur et à la localisation du véhicule hôte dans cette carte, et deuxième partie traite de la détection et du suivi des objets mobiles dans l'environnement (DATMO pour Detection And Tracking of Moving Objects). En utilisant des capteurs laser de grande précision, des résultats importants ont été obtenus par les chercheurs. Cependant, avec des capteurs laser de faible résolution et des données bruitées, le problème est toujours ouvert, en particulier le problème du DATMO. Dans cette thèse nous proposons d'utiliser la vision (mono ou stéréo) couplée à un capteur laser pour résoudre ce problème. La première contribution de cette thèse porte sur l'identification et le développement de trois niveaux de fusion. En fonction du niveau de traitement de l'information capteur avant le processus de fusion, nous les appelons "fusion bas niveau", "fusion au niveau de la détection" et "fusion au niveau du suivi". Pour la fusion bas niveau, nous avons utilisé les grilles d'occupations. Pour la fusion au niveau de la détection, les objets détectés par chaque capteur sont fusionnés pour avoir une liste d'objets fusionnés. La fusion au niveau du suivi requiert le suivi des objets pour chaque capteur et ensuite on réalise la fusion entre les listes d'objets suivis. La deuxième contribution de cette thèse est le développement d'une technique rapide pour trouver les bords de route à partir des données du laser et en utilisant cette information nous supprimons de nombreuses fausses alarmes. Nous avons en effet observé que beaucoup de fausses alarmes apparaissent sur le bord de la route. La troisième contribution de cette thèse est le développement d'une solution complète pour la perception avec un capteur laser et des caméras stéréo-vision et son intégration sur un démonstrateur du projet européen Intersafe-2. Ce projet s'intéresse à la sécurité aux intersections et vise à y réduire les blessures et les accidents mortels. Dans ce projet, nous avons travaillé en collaboration avec Volkswagen, l'Université Technique de Cluj-Napoca, en Roumanie et l'INRIA Paris pour fournir une solution complète de perception et d'évaluation des risques pour le démonstrateur de Volkswagen.Perception is one of important steps for the functioning of an autonomous vehicle or even for a vehicle providing only driver assistance functions. Vehicle observes the external world using its sensors and builds an internal model of the outer environment configuration. It keeps on updating this internal model using latest sensor data. In this setting perception can be divided into two sub parts: first part, called SLAM(Simultaneous Localization And Mapping), is concerned with building an online map of the external environment and localizing the host vehicle in this map, and second part deals with finding moving objects in the environment and tracking them over time and is called DATMO(Detection And Tracking of Moving Objects). Using high resolution and accurate laser scanners successful efforts have been made by many researchers to solve these problems. However, with low resolution or noisy laser scanners solving these problems, especially DATMO, is still a challenge and there are either many false alarms, miss detections or both. In this thesis we propose that by using vision sensor (mono or stereo) along with laser sensor and by developing an effective fusion scheme on an appropriate level, these problems can be greatly reduced. The main contribution of this research is concerned with the identification of three fusion levels and development of fusion techniques for each level for SLAM and DATMO based perception architecture of autonomous vehicles. Depending on the amount of preprocessing required before fusion for each level, we call them low level, object detection level and track level fusion. For low level we propose to use grid based fusion technique and by giving appropriate weights (depending on the sensor properties) to each grid for each sensor a fused grid can be obtained giving better view of the external environment in some sense. For object detection level fusion, lists of objects detected for each sensor are fused to get a list of fused objects where fused objects have more information then their previous versions. We use a Bayesian fusion technique for this level. Track level fusion requires to track moving objects for each sensor separately and then do a fusion between tracks to get fused tracks. Fusion at this level helps remove false tracks. Second contribution of this research is the development of a fast technique of finding road borders from noisy laser data and then using these border information to remove false moving objects. Usually we have observed that many false moving objects appear near the road borders due to sensor noise. If they are not filtered out then they result into many false tracks close to vehicle making vehicle to apply breaks or to issue warning messages to the driver falsely. Third contribution is the development of a complete perception solution for lidar and stereo vision sensors and its intigration on a real vehicle demonstrator used for a European Union project (INTERSAFE-21). This project is concerned with the safety at intersections and aims at the reduction of injury and fatal accidents there. In this project we worked in collaboration with Volkswagen, Technical university of Cluj-Napoca Romania and INRIA Paris to provide a complete perception and risk assessment solution for this project.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF