26 research outputs found

    CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data

    Full text link
    This paper presents a novel method for ground segmentation in Velodyne point clouds. We propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a convolutional neural network (CNN). This general purpose approach is used for segmentation of the sparse point cloud into ground and non-ground points. The LiDAR data are represented as a multi-channel 2D signal where the horizontal axis corresponds to the rotation angle and the vertical axis the indexes channels (i.e. laser beams). Multiple topologies of relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and evaluated using a manually annotated dataset we prepared. The results show significant improvement of performance over the state-of-the-art method by Zhang et al. in terms of speed and also minor improvements in terms of accuracy.Comment: ICRA 2018 submissio

    Transformation-adversarial network for road detection in LIDAR rings, and model-free evidential road grid mapping.

    Get PDF
    International audienceWe propose a deep learning approach to perform road-detection in LIDAR scans, at the point level. Instead of processing a full LIDAR point-cloud, LIDAR rings can be processed individually. To account for the geometrical diversity among LIDAR rings, an homothety rescaling factor can be predicted during the classification, to realign all the LIDAR rings and facilitate the training. This scale factor is learnt in a semi-supervised fashion. A performant classification can then be achieved with a relatively simple system. Furthermore, evidential mass values can be generated for each point from an observation of the conflict at the output of the network, which enables the classification results to be fused in evidential grids. Experiments are done on real-life LIDAR scans that were labelled from a lane-level centimetric map, to evaluate the classification performances

    Traffic Scene Perception for Automated Driving with Top-View Grid Maps

    Get PDF
    Ein automatisiertes Fahrzeug muss sichere, sinnvolle und schnelle Entscheidungen auf Basis seiner Umgebung treffen. Dies benötigt ein genaues und recheneffizientes Modell der Verkehrsumgebung. Mit diesem Umfeldmodell sollen Messungen verschiedener Sensoren fusioniert, gefiltert und nachfolgenden Teilsysteme als kompakte, aber aussagekräftige Information bereitgestellt werden. Diese Arbeit befasst sich mit der Modellierung der Verkehrsszene auf Basis von Top-View Grid Maps. Im Vergleich zu anderen Umfeldmodellen ermöglichen sie eine frühe Fusion von Distanzmessungen aus verschiedenen Quellen mit geringem Rechenaufwand sowie eine explizite Modellierung von Freiraum. Nach der Vorstellung eines Verfahrens zur Bodenoberflächenschätzung, das die Grundlage der Top-View Modellierung darstellt, werden Methoden zur Belegungs- und Elevationskartierung für Grid Maps auf Basis von mehreren, verrauschten, teilweise widersprüchlichen oder fehlenden Distanzmessungen behandelt. Auf der resultierenden, sensorunabhängigen Repräsentation werden anschließend Modelle zur Detektion von Verkehrsteilnehmern sowie zur Schätzung von Szenenfluss, Odometrie und Tracking-Merkmalen untersucht. Untersuchungen auf öffentlich verfügbaren Datensätzen und einem Realfahrzeug zeigen, dass Top-View Grid Maps durch on-board LiDAR Sensorik geschätzt und verlässlich sicherheitskritische Umgebungsinformationen wie Beobachtbarkeit und Befahrbarkeit abgeleitet werden können. Schließlich werden Verkehrsteilnehmer als orientierte Bounding Boxen mit semantischen Klassen, Geschwindigkeiten und Tracking-Merkmalen aus einem gemeinsamen Modell zur Objektdetektion und Flussschätzung auf Basis der Top-View Grid Maps bestimmt

    Detection of Parking Space based on Occupancy Grids in Dynamic Environments for Autonomous Valet Parking

    Get PDF
    학위논문 (석사)-- 서울대학교 융합과학기술대학원 : 융합과학부(지능형융합시스템전공), 2016. 8. 박재흥.최근 자율주행 관련하여 활발한 연구가 진행되고 있는데 특히 자율발렛주차는 중요한 문제 중 하나이다. 자율발렛주차는 차량이 운전자 없이도 스스로 주차 가능한 공간을 찾아 목적지로 정하고, 그 목적지까지 안전하게 주차를 완료하는 기술이다. 이러한 자율발렛주차가 가능하기 위하여 주차장 환경인식이 요구된다. 본 연구는 자율발렛주차를 위한 주차 가능한 공간 인식에 그 목적이 있다. 이에 대한 기존 연구들은 대부분 차량 인식이나 주차구획 인식에만 집중해 있는데, 이는 복잡하고 동적 장애물이 많은 주차장 환경에서 충분한 정보를 제공하지 못한다. 본 논문은 Extended Evidential grid map을 주차 가능한 공간 인식에 적용을 제안한다. Dempster-Shafer이론에 기반한 Evidential grid map은 기존 점유격자지도의 물체 인식뿐만 아니라 동적인 물체를 구별해 낼 수 있다. 그러나 센서 인식범위의 한계와 차량들의 겹침 영역 때문에 주차구획의 점유여부 인식에 어려움이 있다. 본 연구에서는 이를 보완하기 위하여 겹침 영역에 대한 Occupied 확률의 확장을 제안하였다. 또한, 주차구획의 점유여부 판별이 어려운 공간에도 주차가능 후보들을 선정하여 주차가능 공간을 찾기 위한 경로 탐색에 효율성을 높이고자 하였다. 실외 주차장에서 실제 자율주행 자동차의 실험을 통하여 본 연구의 유용성을 확인하였다.1. 서 론 1 1.1 연구 목적 1 1.2 연구 배경 4 2. 자율발렛주차를 위한 격자지도 작성 22 2.1 확장된 증거이론 22 2.2 3차원 point cloud의 센서모델 24 3. 시스템 구성 26 3.1 자율주행자동차 26 3.2 환경인식 27 4. 실험 29 5. 결론 39 참고 문헌 41 Abstract 45Maste

    Evidential deep learning for arbitrary LIDAR object classification in the context of autonomous driving

    Get PDF
    International audienceIn traditional LIDAR processing pipelines, a point-cloud is split into clusters, or objects, which are classified afterwards. This supposes that all the objects obtained by clustering belong to one of the classes that the classifier can recognize, which is hard to guarantee in practice. We thus propose an evidential end-to-end deep neural network to classify LIDAR objects. The system is capable of classifying ambiguous and incoherent objects as unknown, while only having been trained on vehicles and vulnerable road users. This is achieved thanks to an evidential reformulation of generalized logistic regression classifiers, and an online filtering strategy based on statistical assumptions. The training and testing were realized on LIDAR objects which were labelled in a semi-automatic fashion, and collected in different situations thanks to an autonomous driving and perception platform

    Exploitation des données cartographiques pour la perception de véhicules intelligents

    Get PDF
    This thesis is situated in the domains of robotics and data fusion, and concerns geographic information systems. We study the utility of adding digital maps, which model the urban environment in which the vehicle evolves, as a virtual sensor improving the perception results. Indeed, the maps contain a phenomenal quantity of information about the environment : its geometry, topology and additional contextual information. In this work, we extract road surface geometry and building models in order to deduce the context and the characteristics of each detected object. Our method is based on an extension of occupancy grids : the evidential perception grids. It permits to model explicitly the uncertainty related to the map and sensor data. By this means, the approach presents also the advantage of representing homogeneously the data originating from various sources : lidar, camera or maps. The maps are handled on equal terms with the physical sensors. This approach allows us to add geographic information without imputing unduly importance to it, which is essential in presence of errors. In our approach, the information fusion result, stored in a perception grid, is used to predict the stateof environment on the next instant. The fact of estimating the characteristics of dynamic elements does not satisfy the hypothesis of static world. Therefore, it is necessary to adjust the level of certainty attributed to these pieces of information. We do so by applying the temporal discounting. Due to the fact that existing methods are not well suited for this application, we propose a family of discoun toperators that take into account the type of handled information. The studied algorithms have been validated through tests on real data. We have thus developed the prototypes in Matlab and the C++ software based on Pacpus framework. Thanks to them, we present the results of experiments performed in real conditions.La plupart des logiciels contrôlant les véhicules intelligents traite de la compréhension de la scène. De nombreuses méthodes existent actuellement pour percevoir les obstacles de façon automatique. La majorité d’entre elles emploie ainsi les capteurs extéroceptifs comme des caméras ou des lidars. Cette thèse porte sur les domaines de la robotique et de la fusion d’information et s’intéresse aux systèmes d’information géographique. Nous étudions ainsi l’utilité d’ajouter des cartes numériques, qui cartographient le milieu urbain dans lequel évolue le véhicule, en tant que capteur virtuel améliorant les résultats de perception. Les cartes contiennent en effet une quantité phénoménale d’information sur l’environnement : sa géométrie, sa topologie ainsi que d’autres informations contextuelles. Dans nos travaux, nous avons extrait la géométrie des routes et des modèles de bâtiments afin de déduire le contexte et les caractéristiques de chaque objet détecté. Notre méthode se base sur une extension de grilles d’occupations : les grilles de perception crédibilistes. Elle permet de modéliser explicitement les incertitudes liées aux données de cartes et de capteurs. Elle présente également l’avantage de représenter de façon uniforme les données provenant de différentes sources : lidar, caméra ou cartes. Les cartes sont traitées de la même façon que les capteurs physiques. Cette démarche permet d’ajouter les informations géographiques sans pour autant leur donner trop d’importance, ce qui est essentiel en présence d’erreurs. Dans notre approche, le résultat de la fusion d’information contenu dans une grille de perception est utilisé pour prédire l’état de l’environnement à l’instant suivant. Le fait d’estimer les caractéristiques des éléments dynamiques ne satisfait donc plus l’hypothèse du monde statique. Par conséquent, il est nécessaire d’ajuster le niveau de certitude attribué à ces informations. Nous y parvenons en appliquant l’affaiblissement temporel. Étant donné que les méthodes existantes n’étaient pas adaptées à cette application, nous proposons une famille d’opérateurs d’affaiblissement prenant en compte le type d’information traitée. Les algorithmes étudiés ont été validés par des tests sur des données réelles. Nous avons donc développé des prototypes en Matlab et des logiciels en C++ basés sur la plate-forme Pacpus. Grâce à eux nous présentons les résultats des expériences effectués en conditions réelles
    corecore