LiDAR-Based Object Tracking and Shape Estimation

Umfeldwahrnehmung stellt eine Grundvoraussetzung für den sicheren und komfortablen Betrieb automatisierter Fahrzeuge dar. Insbesondere bewegte Verkehrsteilnehmer in der unmittelbaren Fahrzeugumgebung haben dabei große Auswirkungen auf die Wahl einer angemessenen Fahrstrategie. Dies macht ein System zur Objektwahrnehmung notwendig, welches eine robuste und präzise Zustandsschätzung der Fremdfahrzeugbewegung und -geometrie zur Verfügung stellt. Im Kontext des automatisierten Fahrens hat sich das Box-Geometriemodell über die Zeit als Quasistandard durchgesetzt. Allerdings stellt die Box aufgrund der ständig steigenden Anforderungen an Wahrnehmungssysteme inzwischen häufig eine unerwünscht grobe Approximation der tatsächlichen Geometrie anderer Verkehrsteilnehmer dar. Dies motiviert einen Übergang zu genaueren Formrepräsentationen. In der vorliegenden Arbeit wird daher ein probabilistisches Verfahren zur gleichzeitigen Schätzung von starrer Objektform und -bewegung mittels Messdaten eines LiDAR-Sensors vorgestellt. Der Vergleich dreier Freiform-Geometriemodelle mit verschiedenen Detaillierungsgraden (Polygonzug, Dreiecksnetz und Surfel Map) gegenüber dem einfachen Boxmodell zeigt, dass die Reduktion von Modellierungsfehlern in der Objektgeometrie eine robustere und präzisere Parameterschätzung von Objektzuständen ermöglicht. Darüber hinaus können automatisierte Fahrfunktionen, wie beispielsweise ein Park- oder Ausweichassistent, von einem genaueren Wissen über die Fremdobjektform profitieren. Es existieren zwei Einflussgrößen, welche die Auswahl einer angemessenen Formrepräsentation maßgeblich beeinflussen sollten: Beobachtbarkeit (Welchen Detaillierungsgrad lässt die Sensorspezifikation theoretisch zu?) und Modell-Adäquatheit (Wie gut bildet das gegebene Modell die tatsächlichen Beobachtungen ab?). Auf Basis dieser Einflussgrößen wird in der vorliegenden Arbeit eine Strategie zur Modellauswahl vorgestellt, die zur Laufzeit adaptiv das am besten geeignete Formmodell bestimmt. Während die Mehrzahl der Algorithmen zur LiDAR-basierten Objektverfolgung ausschließlich auf Punktmessungen zurückgreift, werden in der vorliegenden Arbeit zwei weitere Arten von Messungen vorgeschlagen: Information über den vermessenen Freiraum wird verwendet, um über Bereiche zu schlussfolgern, welche nicht von Objektgeometrie belegt sein können. Des Weiteren werden LiDAR-Intensitäten einbezogen, um markante Merkmale wie Nummernschilder und Retroreflektoren zu detektieren und über die Zeit zu verfolgen. Eine ausführliche Auswertung auf über 1,5 Stunden von aufgezeichneten Fremdfahrzeugtrajektorien im urbanen Bereich und auf der Autobahn zeigen, dass eine präzise Modellierung der Objektoberfläche die Bewegungsschätzung um bis zu 30%-40% verbessern kann. Darüber hinaus wird gezeigt, dass die vorgestellten Methoden konsistente und hochpräzise Rekonstruktionen von Objektgeometrien generieren können, welche die häufig signifikante Überapproximation durch das einfache Boxmodell vermeiden

Road terrain detection for Advanced Driver Assistance Systems

Kühnl T. Road terrain detection for Advanced Driver Assistance Systems. Bielefeld: Bielefeld University; 2013

Behavioural strategy for indoor mobile robot navigation in dynamic environments

PhD ThesisDevelopment of behavioural strategies for indoor mobile navigation has become a challenging and practical issue in a cluttered indoor environment, such as a hospital or factory, where there are many static and moving objects, including humans and other robots, all of which trying to complete their own specific tasks; some objects may be moving in a similar direction to the robot, whereas others may be moving in the opposite direction. The key requirement for any mobile robot is to avoid colliding with any object which may prevent it from reaching its goal, or as a consequence bring harm to any individual within its workspace. This challenge is further complicated by unobserved objects suddenly appearing in the robots path, particularly when the robot crosses a corridor or an open doorway. Therefore the mobile robot must be able to anticipate such scenarios and manoeuvre quickly to avoid collisions. In this project, a hybrid control architecture has been designed to navigate within dynamic environments. The control system includes three levels namely: deliberative, intermediate and reactive, which work together to achieve short, fast and safe navigation. The deliberative level creates a short and safe path from the current position of the mobile robot to its goal using the wavefront algorithm, estimates the current location of the mobile robot, and extracts the region from which unobserved objects may appear. The intermediate level links the deliberative level and the reactive level, that includes several behaviours for implementing the global path in such a way to avoid any collision. In avoiding dynamic obstacles, the controller has to identify and extract obstacles from the sensor data, estimate their speeds, and then regular its speed and direction to minimize the collision risk and maximize the speed to the goal. The velocity obstacle approach (VO) is considered an easy and simple method for avoiding dynamic obstacles, whilst the collision cone principle is used to detect the collision situation between two circular-shaped objects. However the VO approach has two challenges when applied in indoor environments. The first challenge is extraction of collision cones of non-circular objects from sensor data, in which applying fitting circle methods generally produces large and inaccurate collision cones especially for line-shaped obstacle such as walls. The second challenge is that the mobile robot cannot sometimes move to its goal because all its velocities to the goal are located within collision cones. In this project, a method has been demonstrated to extract the colliii sion cones of circular and non-circular objects using a laser sensor, where the obstacle size and the collision time are considered to weigh the robot velocities. In addition the principle of the virtual obstacle was proposed to minimize the collision risk with unobserved moving obstacles. The simulation and experiments using the proposed control system on a Pioneer mobile robot showed that the mobile robot can successfully avoid static and dynamic obstacles. Furthermore the mobile robot was able to reach its target within an indoor environment without causing any collision or missing the target

Automatic vehicle detection and tracking in aerial video

This thesis is concerned with the challenging tasks of automatic and real-time vehicle detection and tracking from aerial video. The aim of this thesis is to build an automatic system that can accurately localise any vehicles that appear in aerial video frames and track the target vehicles with trackers. Vehicle detection and tracking have many applications and this has been an active area of research during recent years; however, it is still a challenge to deal with certain realistic environments. This thesis develops vehicle detection and tracking algorithms which enhance the robustness of detection and tracking beyond the existing approaches. The basis of the vehicle detection system proposed in this thesis has different object categorisation approaches, with colour and texture features in both point and area template forms. The thesis also proposes a novel Self-Learning Tracking and Detection approach, which is an extension to the existing Tracking Learning Detection (TLD) algorithm. There are a number of challenges in vehicle detection and tracking. The most difficult challenge of detection is distinguishing and clustering the target vehicle from the background objects and noises. Under certain conditions, the images captured from Unmanned Aerial Vehicles (UAVs) are also blurred; for example, turbulence may make the vehicle shake during flight. This thesis tackles these challenges by applying integrated multiple feature descriptors for real-time processing. In this thesis, three vehicle detection approaches are proposed: the HSV-GLCM feature approach, the ISM-SIFT feature approach and the FAST-HoG approach. The general vehicle detection approaches used have highly flexible implicit shape representations. They are based on training samples in both positive and negative sets and use updated classifiers to distinguish the targets. It has been found that the detection results attained by using HSV-GLCM texture features can be affected by blurring problems; the proposed detection algorithms can further segment the edges of the vehicles from the background. Using the point descriptor feature can solve the blurring problem, however, the large amount of information contained in point descriptors can lead to processing times that are too long for real-time applications. So the FAST-HoG approach combining the point feature and the shape feature is proposed. This new approach is able to speed up the process that attains the real-time performance. Finally, a detection approach using HoG with the FAST feature is also proposed. The HoG approach is widely used in object recognition, as it has a strong ability to represent the shape vector of the object. However, the original HoG feature is sensitive to the orientation of the target; this method improves the algorithm by inserting the direction vectors of the targets. For the tracking process, a novel tracking approach was proposed, an extension of the TLD algorithm, in order to track multiple targets. The extended approach upgrades the original system, which can only track a single target, which must be selected before the detection and tracking process. The greatest challenge to vehicle tracking is long-term tracking. The target object can change its appearance during the process and illumination and scale changes can also occur. The original TLD feature assumed that tracking can make errors during the tracking process, and the accumulation of these errors could cause tracking failure, so the original TLD proposed using a learning approach in between the tracking and the detection by adding a pair of inspectors (positive and negative) to constantly estimate errors. This thesis extends the TLD approach with a new detection method in order to achieve multiple-target tracking. A Forward and Backward Tracking approach has been proposed to eliminate tracking errors and other problems such as occlusion. The main purpose of the proposed tracking system is to learn the features of the targets during tracking and re-train the detection classifier for further processes. This thesis puts particular emphasis on vehicle detection and tracking in different extreme scenarios such as crowed highway vehicle detection, blurred images and changes in the appearance of the targets. Compared with currently existing detection and tracking approaches, the proposed approaches demonstrate a robust increase in accuracy in each scenario

Lane estimation for autonomous vehicles using vision and LIDAR

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 109-114).Autonomous ground vehicles, or self-driving cars, require a high level of situational awareness in order to operate safely and eciently in real-world conditions. A system able to quickly and reliably estimate the location of the roadway and its lanes based upon local sensor data would be a valuable asset both to fully autonomous vehicles as well as driver assistance technologies. To be most useful, the system must accommodate a variety of roadways, a range of weather and lighting conditions, and highly dynamic scenes with other vehicles and moving objects. Lane estimation can be modeled as a curve estimation problem, where sensor data provides partial and noisy observations of curves. The number of curves to estimate may be initially unknown and many of the observations may be outliers and false detections (e.g., due to tree shadows or lens are). The challenge is to detect lanes when and where they exist, and to update the lane estimates as new observations are received. This thesis describes algorithms for feature detection and curve estimation, as well as a novel curve representation that permits fast and ecient estimation while rejecting outliers. Locally observed road paint and curb features are fused together in a lane estimation framework that detects and estimates all nearby travel lanes.(cont.) The system handles roads with complex geometries and makes no assumptions about the position and orientation of the vehicle with respect to the roadway. Early versions of these algorithms successfully guided a fully autonomous Land Rover LR3 through the 2007 DARPA Urban Challenge, a 90km urban race course, at speeds up to 40 km/h amidst moving traffic. We evaluate these and subsequent versions with a ground truth dataset containing manually labeled lane geometries for every moment of vehicle travel in two large and diverse datasets that include more than 300,000 images and 44km of roadway. The results illustrate the capabilities of our algorithms for robust lane estimation in the face of challenging conditions and unknown roadways.by Albert S. Huang.Ph.D

Development of a probabilistic perception system for camera-lidar sensor fusion

La estimación de profundidad usando diferentes sensores es uno de los desafíos clave para dotar a las máquinas autónomas de sólidas capacidades de percepción robótica. Ha habido un avance sobresaliente en el desarrollo de técnicas de estimación de profundidad unimodales basadas en cámaras monoculares, debido a su alta resolución o sensores LiDAR, debido a los datos geométricos precisos que proporcionan. Sin embargo, cada uno de ellos presenta inconvenientes inherentes, como la alta sensibilidad a los cambios en las condiciones de iluminación en el caso delas cámaras y la resolución limitada de los sensores LiDAR. La fusión de sensores se puede utilizar para combinar los méritos y compensar las desventajas de estos dos tipos de sensores. Sin embargo, los métodos de fusión actuales funcionan a un alto nivel. Procesan los flujos de datos de los sensores de forma independiente y combinan las estimaciones de alto nivel obtenidas para cada sensor. En este proyecto, abordamos el problema en un nivel bajo, fusionando los flujos de sensores sin procesar, obteniendo así estimaciones de profundidad que son densas y precisas, y pueden usarse como una fuente de datos multimodal unificada para problemas de estimación de nivel superior. Este trabajo propone un modelo de campo aleatorio condicional (CRF) con múltiples potenciales de geometría y apariencia que representa a la perfección el problema de estimar mapas de profundidad densos a partir de datos de cámara y LiDAR. El modelo se puede optimizar de manera eficiente utilizando el algoritmo Conjúgate Gradient Squared (CGS). El método propuesto se evalúa y compara utilizando el conjunto de datos proporcionado por KITTI Datset. Adicionalmente, se evalúa cualitativamente el modelo, usando datos adquiridos por el autor de esté trabajoMulti-modal depth estimation is one of the key challenges for endowing autonomous machines with robust robotic perception capabilities. There has been an outstanding advance in the development of uni-modal depth estimation techniques based on either monocular cameras, because of their rich resolution or LiDAR sensors due to the precise geometric data they provide. However, each of them suffers from some inherent drawbacks like high sensitivity to changes in illumination conditions in the case of cameras and limited resolution for the LiDARs. Sensor fusion can be used to combine the merits and compensate the downsides of these two kinds of sensors. Nevertheless, current fusion methods work at a high level. They processes sensor data streams independently and combine the high level estimates obtained for each sensor. In this thesis, I tackle the problem at a low level, fusing the raw sensor streams, thus obtaining depth estimates which are both dense and precise, and can be used as a unified multi-modal data source for higher level estimation problems. This work proposes a Conditional Random Field (CRF) model with multiple geometry and appearance potentials that seamlessly represents the problem of estimating dense depth maps from camera and LiDAR data. The model can be optimized efficiently using the Conjugate Gradient Squared (CGS) algorithm. The proposed method was evaluated and compared with the state-of-the-art using the commonly used KITTI benchmark dataset. In addition, the model is qualitatively evaluated using data acquired by the author of this work.MaestríaMagíster en Ingeniería de Desarrollo de Producto

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Visuelle Detektion unabhängig bewegter Objekte durch einen bewegten monokularen Beobachter

The development of a driver assistant system supporting drivers in complex intersection situations would be a major achievement for traffic safety, since many traffic accidents happen in such situations. While this is a highly complex task, which is still not accomplished, this thesis focused on one important and obligatory aspect of such systems: The visual detection of independently moving objects. Information about moving objects can, for example, be used in an attention guidance system, which is a central component of any complete intersection assistant system. The decision to base such a system on visual input had two reasons: (i) Humans gather their information to a large extent visually and (ii) cameras are inexpensive and already widely used in luxury and professional vehicles for specific applications. Mimicking the articulated human head and eyes, agile camera systems are desirable. To avoid heavy and sensitive stereo rigs, a small and lightweight monocular camera system mounted on a pan-tilt unit has been chosen as input device. In this thesis information about moving objects has been used to develop a prototype of an attention guidance system. It is based on the analysis of sequences from a single freely moving camera and on measurements from inertial sensors rigidly coupled with the camera system.Die Entwicklung eines Fahrerassistenzsystems, welches den Fahrer in komplexen Kreuzungssituationen unterstützt, wäre ein wichtiger Beitrag zur Verkehrssicherheit, da sehr viele Unfälle in solchen Situationen passieren. Dies ist eine hochgradig komplexe Aufgabe und daher liegt der Fokus dieser Arbeit auf einen wichtigen und notwendigen Aspekt solcher Systeme: Die visuelle Detektion unabhängig bewegter Objekte. Informationen über bewegte Objekte können z.B. für ein System zur Aufmerksamkeitssteuerung verwendet werden. Solch ein System ist ein integraler Bestandteil eines jeden kompletten Kreuzungsassistenzssystems. Zwei Gründe haben zu der Entscheidung geführt, das System auf visuellen Daten zu stützen: (i) Der Mensch sammelt seine Informationen zum Großteil visuell und (ii) Kameras sind zum Einen günstig und zum Anderen bereits jetzt in vielen Fahrzeugen verfügbar. Agile Kamerasysteme sind nötig um den beweglichen menschlichen Kopf zu imitieren. Die Wahl einer kleinen und leichten monokularen Kamera, die auf einer Schwenk-Neige-Einheit montiert ist, vermeidet die Verwendung von schweren und empfindlichen Stereokamerasystemen. Mit den Informationen über bewegte Objekte ist in dieser Arbeit der Prototyp eines Fahrerassistenzsystems Aufmerksamkeitssteuerung entwickelt worden. Das System basiert auf der Analyse von Bildsequenzen einer frei bewegten Kamera und auf Messungen von der mit der Kamera starr gekoppelten Inertialsensorik