Omnidirectional Stereo Vision for Autonomous Vehicles

Environment perception with cameras is an important requirement for many applications for autonomous vehicles and robots. This work presents a stereoscopic omnidirectional camera system for autonomous vehicles which resolves the problem of a limited field of view and provides a 360° panoramic view of the environment. We present a new projection model for these cameras and show that the camera setup overcomes major drawbacks of traditional perspective cameras in many applications

Novel Camera Architectures for Localization and Mapping on Intelligent Mobile Platforms

Self-localization and environment mapping play a very important role in many robotics application such as autonomous driving and mixed reality consumer products. Although the most powerful solutions rely on a multitude of sensors including lidars and camera, the community maintains a high interest in developing cost-effective, purely vision-based localization and mapping approaches. The core problem of standard vision-only solutions is accuracy and robustness, especially in challenging visual conditions. The thesis aims to introduce new solutions to localization and mapping problems on intelligent mobile devices by taking advantages of novel camera architectures. The thesis investigates on using surround-view multi-camera systems, which combine the benefits of omni-directional measurements with a sufficient baseline for producing measurements in metric scale, and event cameras, that perform well under challenging illumination conditions and have high temporal resolutions. The thesis starts by looking into the motion estimation framework with multi-perspective camera systems. The framework could be divided into two sub-parts, a front-end module that initializes motion and estimates absolute pose after bootstrapping, and a back-end module that refines the estimate over a larger-scale sequence. First, the thesis proposes a complete real-time pipeline for visual odometry with non-overlapping, multi-perspective camera systems, and in particular presents a solution to the scale initialization problem, in order to solve the unobservability of metric scale under degenerate cases with such systems. Second, the thesis focuses on the further improvement of front-end relative pose estimation for vehicle-mounted surround-view multi-camera systems. It presents a new, reliable solution able to handle all kinds of relative displacements in the plane despite the possibly non-holonomic characteristics, and furthermore introduces a novel two-view optimization scheme which minimizes a geometrically relevant error without relying on 3D points related optimization variables. Third, the thesis explores the continues-time parametrization for exact modelling of non-holonomic ground vehicle trajectories in the back-end optimization of visual SLAM pipeline. It demonstrates the use of B-splines for an exact imposition of smooth, non-holonomic trajectories inside the 6 DoF bundle adjustment, and show that a significant improvement in robustness and accuracy in degrading visual conditions can be achieved. In order to deal with challenges in scenarios with high dynamics, low texture distinctiveness, or challenging illumination conditions, the thesis focuses on the solution to localization and mapping problem on Autonomous Ground Vehicle(AGV) using event cameras. Inspired by the time-continuous parametrizations of image warping functions introduced by previous works, the thesis proposes two new algorithms to tackle several motion estimation problems by performing contrast maximization approach. It firstly looks at the fronto-parallel motion estimation of an event camera, in stark contrast to the prior art, a globally optimal solution to this motion estimation problem is derived by using a branch-and-bound optimization scheme. Then, the thesis introduces a new solution to handle the localization and mapping problem of single event camera by continuous ray warping and volumetric contrast maximization, which can perform joint optimization over motion and structure for cameras exerting both translational and rotational displacements in an arbitrarily structured environment. The present thesis thus makes important contributions on both front-end and back-end of SLAM pipelines based on novel, promising camera architectures

Omnidirectional Stereo Vision for Autonomous Vehicles

Environment perception with cameras is an important requirement for many applications for autonomous vehicles and robots. This work presents a stereoscopic omnidirectional camera system for autonomous vehicles which resolves the problem of a limited field of view and provides a 360° panoramic view of the environment. We present a new projection model for these cameras and show that the camera setup overcomes major drawbacks of traditional perspective cameras in many applications

Distributed Robotic Vision for Calibration, Localisation, and Mapping

This dissertation explores distributed algorithms for calibration, localisation, and mapping in the context of a multi-robot network equipped with cameras and onboard processing, comparing against centralised alternatives where all data is transmitted to a singular external node on which processing occurs. With the rise of large-scale camera networks, and as low-cost on-board processing becomes increasingly feasible in robotics networks, distributed algorithms are becoming important for robustness and scalability. Standard solutions to multi-camera computer vision require the data from all nodes to be processed at a central node which represents a significant single point of failure and incurs infeasible communication costs. Distributed solutions solve these issues by spreading the work over the entire network, operating only on local calculations and direct communication with nearby neighbours. This research considers a framework for a distributed robotic vision platform for calibration, localisation, mapping tasks where three main stages are identified: an initialisation stage where calibration and localisation are performed in a distributed manner, a local tracking stage where visual odometry is performed without inter-robot communication, and a global mapping stage where global alignment and optimisation strategies are applied. In consideration of this framework, this research investigates how algorithms can be developed to produce fundamentally distributed solutions, designed to minimise computational complexity whilst maintaining excellent performance, and designed to operate effectively in the long term. Therefore, three primary objectives are sought aligning with these three stages

Robust Estimation of Motion Parameters and Scene Geometry : Minimal Solvers and Convexification of Regularisers for Low-Rank Approximation

In the dawning age of autonomous driving, accurate and robust tracking of vehicles is a quintessential part. This is inextricably linked with the problem of Simultaneous Localisation and Mapping (SLAM), in which one tries to determine the position of a vehicle relative to its surroundings without prior knowledge of them. The more you know about the object you wish to track—through sensors or mechanical construction—the more likely you are to get good positioning estimates. In the first part of this thesis, we explore new ways of improving positioning for vehicles travelling on a planar surface. This is done in several different ways: first, we generalise the work done for monocular vision to include two cameras, we propose ways of speeding up the estimation time with polynomial solvers, and we develop an auto-calibration method to cope with radially distorted images, without enforcing pre-calibration procedures.We continue to investigate the case of constrained motion—this time using auxiliary data from inertial measurement units (IMUs) to improve positioning of unmanned aerial vehicles (UAVs). The proposed methods improve the state-of-the-art for partially calibrated cases (with unknown focal length) for indoor navigation. Furthermore, we propose the first-ever real-time compatible minimal solver for simultaneous estimation of radial distortion profile, focal length, and motion parameters while utilising the IMU data.In the third and final part of this thesis, we develop a bilinear framework for low-rank regularisation, with global optimality guarantees under certain conditions. We also show equivalence between the linear and the bilinear framework, in the sense that the objectives are equal. This enables users of alternating direction method of multipliers (ADMM)—or other subgradient or splitting methods—to transition to the new framework, while being able to enjoy the benefits of second order methods. Furthermore, we propose a novel regulariser fusing two popular methods. This way we are able to combine the best of two worlds by encouraging bias reduction while enforcing low-rank solutions

The design of a robust 3D Reconstruction system for video sequences in non controlled environments

Along this thesis, a novel and robust approach for obtaining 3D models from video sequences captured with hand-held cameras is adressed. This work defines a fully automatic pipeline that is able to deal with diferent types of sequences and acquiring devices. The designed and implemented system follows a divide and conquer approach. An smart frame decimation process reduces the temporal redundancy of the input video sequence and selects the best conditioned frames for the reconstruction step. Next, the video is split into overlapped clips with a fixed and small number of Key-frames. This allows to parallelize the Structure and Motion process which translates into a dramatic reduction in the computational complexity. The short length of the clips allows an intensive search for the best solution at each step of the reconstruction, which improves the overall system performance. The process of feature tracking is embedded within the reconstruction loop for each clip as a difference with other approaches. The last contribution of this thesis is a final registration step that merges all the processed clips to the same coordinate frame. This last step consists on a set of linear algorithms that combine information of the structure (3D points) and motion (cameras) shared by partial reconstructions of the same static scene to more accurately estimate their registration to the same coordinate system. The performance for the presented algorithm as well as for the global system is demonstrated in experiments with real data

Visual Odometry and Traversability Analysis for Wheeled Robots in Complex Environments

Durch die technische Entwicklung im Bereich der radbasierten mobilen Roboter (WMRs) erweitern sich deren Anwendungsszenarien. Neben den eher strukturierten industriellen und häuslichen Umgebungen sind nun komplexere städtische Szenarien oder Außenbereiche mögliche Einsatzgebiete. Einer dieser neuen Anwendungsfälle wird in dieser Arbeit beschrieben: ein intelligenter persönlicher Mobilitätsassistent, basierend auf einem elektrischen Rollator. Ein solches System hat mehrere Anforderungen: Es muss sicher, robust, leicht und preiswert sein und sollte in der Lage sein, in Echtzeit zu navigieren, um eine direkte physische Interaktion mit dem Benutzer zu ermöglichen. Da diese Eigenschaften für fast alle Arten von WMRs wünschenswert sind, können alle in dieser Arbeit präsentierten Methoden auch mit anderen Typen von WMRs verwendet werden. Zuerst wird eine visuelle Odometriemethode vorgestellt, welche auf die Arbeit mit einer nach unten gerichteten RGB-D-Kamera ausgelegt ist. Hierzu wird die Umgebung auf die Bodenebene projiziert, um eine 2-dimensionale Repräsentation zu erhalten. Nun wird ein effizientes Bildausrichtungsverfahren verwendet, um die Fahrzeugbewegung aus aufeinander folgenden Bildern zu schätzen. Da das Verfahren für den Einsatz auf einem WMR ausgelegt ist, können weitere Annahmen verwendet werden, um die Genauigkeit der visuellen Odometrie zu verbessern. Für einen nicht-holonomischen WMR mit einem bekannten Fahrzeugmodell, entweder Differentialantrieb, Skid-Lenkung oder Ackermann-Lenkung, können die Bewegungsparameter direkt aus den Bilddaten geschätzt werden. Dies verbessert die Genauigkeit und Robustheit des Verfahrens erheblich. Zusätzlich wird eine Ausreißererkennung vorgestellt, die im Modellraum, d.h. den Bewegungsparametern des kinematischen Models, arbeitet. Üblicherweise wird die Ausreißererkennung im Datenraum, d.h. auf den Bildpunkten, durchgeführt. Mittels der Projektion der Umgebung auf die Bodenebene kann auch eine Höhenkarte der Umgebung erstellt werde. Es wird untersucht, ob diese Karte, in Verbindung mit einem detaillierten Fahrzeugmodell, zur Abschätzung zukünftiger Fahrzeugposen verwendet werden kann. Durch die Verwendung einer gemeinsamen bildbasierten Darstellung der Umgebung und des Fahrzeugs wird eine sehr effiziente und dennoch sehr genaue Posenschätzmethode vorgeschlagen. Da die Befahrbarkeit eines Bereichs durch die Fahrzeugposen und mögliche Kollisionen bestimmt werden kann, wird diese Methode für eine neue echtzeitfähige Pfadplanung verwendet. Aus der Fahrzeugpose werden verschiedene Sicherheitskriterien bestimmt, die als Heuristik für einen A*-ähnlichen Planer verwendet werden. Hierzu werden mithilfe des kinematischen Models mögliche zukünftige Fahrzeugposen ermittelt und für jede dieser Posen ein Befahrbarkeitswert berechnet.Das endgültige System ermöglicht eine sichere und robuste Echtzeit-Navigation auch in schwierigen Innen- und Außenumgebungen.The application of wheeled mobile robots (WMRs) is currently expanding from rather controlled industrial or domestic scenarios into more complex urban or outdoor environments, allowing a variety of new use cases. One of these new use cases is described in this thesis: An intelligent personal mobility assistant, based on an electrical rollator. Such a system comes with several requirements: It must be safe and robust, lightweight, inexpensive and should be able to navigate in real-time in order to allow direct physical interaction with the user. As these properties are desirable for most WMRs, all methods proposed in this thesis can also be used with other WMR platforms.First, a visual odometry method is presented, which is tailored to work with a downward facing RGB-D camera. It projects the environment onto a ground plane image and uses an efficient image alignment method to estimate the vehicle motion from consecutive images. As the method is designed for use on a WMR, further constraints can be employed to improve the accuracy of the visual odometry. For a non-holonomic WMR with a known vehicle model, either differential drive, skid steering or Ackermann, the motion parameters of the corresponding kinematic model, instead of the generic motion parameters, can be estimated directly from the image data. This significantly improves the accuracyand robustness of the method. Additionally, an outlier rejection scheme is presented that operates in model space, i.e. the motion parameters of the kinematic model, instead of data space, i.e. image pixels. Furthermore, the projection of the environment onto the ground plane can also be used to create an elevation map of the environment. It is investigated if this map, in conjunction with a detailed vehicle model, can be used to estimate future vehicle poses. By using a common image-based representation of the environment and the vehicle, a very efficient and still highly accurate pose estimation method is proposed. Since the traversability of an area can be determined by the vehicle poses and potential collisions, the pose estimation method is employed to create a novel real-time path planning method. The detailed vehicle model is extended to also represent the vehicle’s chassis for collision detection. Guided by an A*-like planner, a search graph is constructed by propagating the vehicle using its kinematic model to possible future poses and calculating a traversability score for each of these poses. The final system performs safe and robust real-time navigation even in challenging indoor and outdoor environments

Homography-Based Positioning and Planar Motion Recovery

Planar motion is an important and frequently occurring situation in mobile robotics applications. This thesis concerns estimation of ego-motion and pose of a single downwards oriented camera under the assumptions of planar motion and known internal camera parameters. The so called essential matrix (or its uncalibrated counterpart, the fundamental matrix) is frequently used in computer vision applications to compute a reconstruction in 3D of the camera locations and the observed scene. However, if the observed points are expected to lie on a plane - e.g. the ground plane - this makes the determination of these matrices an ill-posed problem. Instead, methods based on homographies are better suited to this situation.One section of this thesis is concerned with the extraction of the camera pose and ego-motion from such homographies. We present both a direct SVD-based method and an iterative method, which both solve this problem. The iterative method is extended to allow simultaneous determination of the camera tilt from several homographies obeying the same planar motion model. This extension improves the robustness of the original method, and it provides consistent tilt estimates for the frames that are used for the estimation. The methods are evaluated using experiments on both real and synthetic data.Another part of the thesis deals with the problem of computing the homographies from point correspondences. By using conventional homography estimation methods for this, the resulting homography is of a too general class and is not guaranteed to be compatible with the planar motion assumption. For this reason, we enforce the planar motion model at the homography estimation stage with the help of a new homography solver using a number of polynomial constraints on the entries of the homography matrix. In addition to giving a homography of the right type, this method uses only \num{2.5} point correspondences instead of the conventional four, which is good \eg{} when used in a RANSAC framework for outlier removal