Vehicle Tracking and Motion Estimation Based on Stereo Vision Sequences

In this dissertation, a novel approach for estimating trajectories of road vehicles such as cars, vans, or motorbikes, based on stereo image sequences is presented. Moving objects are detected and reliably tracked in real-time from within a moving car. The resulting information on the pose and motion state of other moving objects with respect to the own vehicle is an essential basis for future driver assistance and safety systems, e.g., for collision prediction. The focus of this contribution is on oncoming traffic, while most existing work in the literature addresses tracking the lead vehicle. The overall approach is generic and scalable to a variety of traffic scenes including inner city, country road, and highway scenarios. A considerable part of this thesis addresses oncoming traffic at urban intersections. The parameters to be estimated include the 3D position and orientation of an object relative to the ego-vehicle, as well as the object's shape, dimension, velocity, acceleration and the rotational velocity (yaw rate). The key idea is to derive these parameters from a set of tracked 3D points on the object's surface, which are registered to a time-consistent object coordinate system, by means of an extended Kalman filter. Combining the rigid 3D point cloud model with the dynamic model of a vehicle is one main contribution of this thesis. Vehicle tracking at intersections requires covering a wide range of different object dynamics, since vehicles can turn quickly. Three different approaches for tracking objects during highly dynamic turn maneuvers up to extreme maneuvers such as skidding are presented and compared. These approaches allow for an online adaptation of the filter parameter values, overcoming manual parameter tuning depending on the dynamics of the tracked object in the scene. This is the second main contribution. Further issues include the introduction of two initialization methods, a robust outlier handling, a probabilistic approach for assigning new points to a tracked object, as well as mid-level fusion of the vision-based approach with a radar sensor. The overall system is systematically evaluated both on simulated and real-world data. The experimental results show the proposed system is able to accurately estimate the object pose and motion parameters in a variety of challenging situations, including night scenes, quick turn maneuvers, and partial occlusions. The limits of the system are also carefully investigated.In dieser Dissertation wird ein Ansatz zur Trajektorienschätzung von Straßenfahrzeugen (PKW, Lieferwagen, Motorräder,...) anhand von Stereo-Bildfolgen vorgestellt. Bewegte Objekte werden in Echtzeit aus einem fahrenden Auto heraus automatisch detektiert, vermessen und deren Bewegungszustand relativ zum eigenen Fahrzeug zuverlässig bestimmt. Die gewonnenen Informationen liefern einen entscheidenden Grundstein für zukünftige Fahrerassistenz- und Sicherheitssysteme im Automobilbereich, beispielsweise zur Kollisionsprädiktion. Während der Großteil der existierenden Literatur das Detektieren und Verfolgen vorausfahrender Fahrzeuge in Autobahnszenarien adressiert, setzt diese Arbeit einen Schwerpunkt auf den Gegenverkehr, speziell an städtischen Kreuzungen. Der Ansatz ist jedoch grundsätzlich generisch und skalierbar für eine Vielzahl an Verkehrssituationen (Innenstadt, Landstraße, Autobahn). Die zu schätzenden Parameter beinhalten die räumliche Lage des anderen Fahrzeugs relativ zum eigenen Fahrzeug, die Objekt-Geschwindigkeit und -Längsbeschleunigung, sowie die Rotationsgeschwindigkeit (Gierrate) des beobachteten Objektes. Zusätzlich werden die Objektabmaße sowie die Objektform rekonstruiert. Die Grundidee ist es, diese Parameter anhand der Transformation von beobachteten 3D Punkten, welche eine ortsfeste Position auf der Objektoberfläche besitzen, mittels eines rekursiven Schätzers (Kalman Filter) zu bestimmen. Ein wesentlicher Beitrag dieser Arbeit liegt in der Kombination des Starrkörpermodells der Punktewolke mit einem Fahrzeugbewegungsmodell. An Kreuzungen können sehr unterschiedliche Dynamiken auftreten, von einer Geradeausfahrt mit konstanter Geschwindigkeit bis hin zum raschen Abbiegen. Um eine manuelle Parameteradaption abhängig von der jeweiligen Szene zu vermeiden, werden drei verschiedene Ansätze zur automatisierten Anpassung der Filterparameter an die vorliegende Situation vorgestellt und verglichen. Dies stellt den zweiten Hauptbeitrag der Arbeit dar. Weitere wichtige Beiträge sind zwei alternative Initialisierungsmethoden, eine robuste Ausreißerbehandlung, ein probabilistischer Ansatz zur Zuordnung neuer Objektpunkte, sowie die Fusion des bildbasierten Verfahrens mit einem Radar-Sensor. Das Gesamtsystem wird im Rahmen dieser Arbeit systematisch anhand von simulierten und realen Straßenverkehrsszenen evaluiert. Die Ergebnisse zeigen, dass das vorgestellte Verfahren in der Lage ist, die unbekannten Objektparameter auch unter schwierigen Umgebungsbedingungen, beispielsweise bei Nacht, schnellen Abbiegemanövern oder unter Teilverdeckungen, sehr präzise zu schätzen. Die Grenzen des Systems werden ebenfalls sorgfältig untersucht

Efficient stereo matching and obstacle detection using edges in images from a moving vehicle

Fast and robust obstacle detection is a crucial task for autonomous mobile robots. Current approaches for obstacle detection in autonomous cars are based on the use of LIDAR or computer vision. In this thesis computer vision is selected due to its low-power and passive nature. This thesis proposes the use of edges in images to reduce the required storage and processing. Most current approaches are based on dense maps, where all the pixels in the image are used, but this places a heavy load on the storage and processing capacity of the system. This makes dense approaches unsuitable for embedded systems, for which only limited amounts of memory and processing power are available. This motivates us to use sparse maps based on the edges in an image. Typically edge pixels represent a small percentage of the input image yet they are able to represent most of the image semantics. In this thesis two approaches for the use of edges to obtain disparity maps are proposed and one approach for identifying obstacles given edge-based disparities. The first approach proposes a modification to the Census Transform in order to incorporate a similarity measure. This similarity measure behaves as a threshold on the gradient, resulting in the identification of high gradient areas. The identification of these high gradient areas helps to reduce the search space in an area-based stereo-matching approach. Additionally, the Complete Rank Transform is evaluated for the first time in the context of stereo-matching. An area-based local stereo-matching approach is used to evaluate and compare the performance of these pixel descriptors. The second approach proposes a new approach for the computation of edge-disparities. Instead of first detecting the edges and then reducing the search space, the proposed approach detects the edges and computes the disparities at the same time. The approach extends the fast and robust Edge Drawing edge detector to run simultaneously across the stereo pair. By doing this the number of matched pixels and the required operations are reduced as the descriptors and costs are only computed for a fraction of the edge pixels (anchor points). Then the image gradient is used to propagate the disparities from the matched anchor points along the gradients, resulting in one-voxel wide chains of 3D points with connectivity information. The third proposed algorithm takes as input edge-based disparity maps which are compact and yet retain the semantic representation of the captured scene. This approach estimates the ground plane, clusters the edges into individual obstacles and then computes the image stixels which allow the identification of the free and occupied space in the captured stereo-views. Previous approaches for the computation of stixels use dense disparity maps or occupancy grids. Moreover they are unable to identify more than one stixel per column, whereas our approach can. This means that it can identify partially occluded objects. The proposed approach is tested on a public-domain dataset. Results for accuracy and performance are presented. The obtained results show that by using image edges it is possible to reduce the required processing and storage while obtaining accuracies comparable to those obtained by dense approaches

Multi-Sensor Data Fusion for Robust Environment Reconstruction in Autonomous Vehicle Applications

In autonomous vehicle systems, understanding the surrounding environment is mandatory for an intelligent vehicle to make every decision of movement on the road. Knowledge about the neighboring environment enables the vehicle to detect moving objects, especially irregular events such as jaywalking, sudden lane change of the vehicle etc. to avoid collision. This local situation awareness mostly depends on the advanced sensors (e.g. camera, LIDAR, RADAR) added to the vehicle. The main focus of this work is to formulate a problem of reconstructing the vehicle environment using point cloud data from the LIDAR and RGB color images from the camera. Based on a widely used point cloud registration tool such as iterated closest point (ICP), an expectation-maximization (EM)-ICP technique has been proposed to automatically mosaic multiple point cloud sets into a larger one. Motion trajectories of the moving objects are analyzed to address the issue of irregularity detection. Another contribution of this work is the utilization of fusion of color information (from RGB color images captured by the camera) with the three-dimensional point cloud data for better representation of the environment. For better understanding of the surrounding environment, histogram of oriented gradient (HOG) based techniques are exploited to detect pedestrians and vehicles.;Using both camera and LIDAR, an autonomous vehicle can gather information and reconstruct the map of the surrounding environment up to a certain distance. Capability of communicating and cooperating among vehicles can improve the automated driving decisions by providing extended and more precise view of the surroundings. In this work, a transmission power control algorithm is studied along with the adaptive content control algorithm to achieve a more accurate map of the vehicle environment. To exchange the local sensor data among the vehicles, an adaptive communication scheme is proposed that controls the lengths and the contents of the messages depending on the load of the communication channel. The exchange of this information can extend the tracking region of a vehicle beyond the area sensed by its own sensors. In this experiment, a combined effect of power control, and message length and content control algorithm is exploited to improve the map\u27s accuracy of the surroundings in a cooperative automated vehicle system