3D city scale reconstruction using wide area motion imagery

3D reconstruction is one of the most challenging but also most necessary part of computer vision. It is generally applied everywhere, from remote sensing to medical imaging and multimedia. Wide Area Motion Imagery is a field that has gained traction over the recent years. It consists in using an airborne large field of view sensor to cover a typically over a square kilometer area for each captured image. This is particularly valuable data for analysis but the amount of information is overwhelming for any human analyst. Algorithms to efficiently and automatically extract information are therefore needed and 3D reconstruction plays a critical part in it, along with detection and tracking. This dissertation work presents novel reconstruction algorithms to compute a 3D probabilistic space, a set of experiments to efficiently extract photo realistic 3D point clouds and a range of transformations for possible applications of the generated 3D data to filtering, data compression and mapping. The algorithms have been successfully tested on our own datasets provided by Transparent Sky and this thesis work also proposes methods to evaluate accuracy, completeness and photo-consistency. The generated data has been successfully used to improve detection and tracking performances, and allows data compression and extrapolation by generating synthetic images from new point of view, and data augmentation with the inferred occlusion areas.Includes bibliographical reference

A Unified Framework for Constrained Visual-Inertial Navigation with Guaranteed Convergence

This Thesis focuses on some challenging problems in applied Computer Vision: motion estimation of a vehicle by fusing measurements coming from a low-accuracy Inertial Measurement Unit (IMU) and a Stereo Vision System (SVS), and the robust motion estimation of an object moving in front of a camera by using probabilistic techniques. In the first problem, a vehicle supposed moving in an unstructured environment is considered. The vehicle is equipped with a stereo vision system and an inertial measurements unit. For the purposes of the work, unstructured environment means that no prior knowledge is available about the scene being observed, nor about the motion. For the goal of sensor fusion, the work relies on the use of epipolar constraints as output maps in a loose-coupling approach of the measurements provided by the two sensor suites. This means that the state vector does not contain any information about the environment and associated keypoints being observed and its dimension is kept constant along the whole estimation task. The observability analysis is proposed in order to define the asymptotic convergence properties of the parameter estimates and the motion requirements for full observability of the system. It will be shown that the existing techniques of visual-inertial navigation that rely on (features-based) visual constraints can be unified under such convergence properties. Simulations and experimental results are summarized that confirm the theoretical conclusions. In the second problem, the motion estimation algorithm takes advantage from the knowledge of the geometry of the tracked object. Similar problems are encountered for example in the framework of autonomous formation flight and aerial refueling, relative localization with respect to known objects and/or patterns, and so on. The problem is challenged with respect to the classical literature, because it is assumed that the system does not know a priori the association between measurements and projections of the visible parts of the object and reformulates the problem (usually solved via algebraic techniques or iterative optimizations) into a stochastic nonlinear filtering framework. The system is designed to be robust with respect to outliers contamination in the data and object occlusions. The approach is demonstrated with the problem of hand palm pose estimation and motion tracking during reach-and-grasp operations and the related results are presented

RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments

It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems. Firstly, we propose an IMU-PARSAC algorithm which can robustly detect and match keypoints in a two-stage process. In the first state, landmarks are matched with new keypoints using visual and IMU measurements. We collect statistical information from the matching and then guide the intra-keypoint matching in the second stage. Secondly, to handle the problem of pure rotation, we detect the motion type and adapt the deferred-triangulation technique during the data-association process. We make the pure-rotational frames into the special subframes. When solving the visual-inertial bundle adjustment, they provide additional constraints to the pure-rotational motion. We evaluate the proposed VIO system on public datasets. Experiments show the proposed RD-VIO has obvious advantages over other methods in dynamic environments

Exploiting Structural Regularities and Beyond: Vision-based Localization and Mapping in Man-Made Environments

Image-based estimation of camera motion, known as visual odometry (VO), plays a very important role in many robotic applications such as control and navigation of unmanned mobile robots, especially when no external navigation reference signal is available. The core problem of VO is the estimation of the camera’s ego-motion (i.e. tracking) either between successive frames, namely relative pose estimation, or with respect to a global map, namely absolute pose estimation. This thesis aims to develop efficient, accurate and robust VO solutions by taking advantage of structural regularities in man-made environments, such as piece-wise planar structures, Manhattan World and more generally, contours and edges. Furthermore, to handle challenging scenarios that are beyond the limits of classical sensor based VO solutions, we investigate a recently emerging sensor — the event camera and study on event-based mapping — one of the key problems in the event-based VO/SLAM. The main achievements are summarized as follows. First, we revisit an old topic on relative pose estimation: accurately and robustly estimating the fundamental matrix given a collection of independently estimated homograhies. Three classical methods are reviewed and then we show a simple but nontrivial two-step normalization within the direct linear method that achieves similar performance to the less attractive and more computationally intensive hallucinated points based method. Second, an efficient 3D rotation estimation algorithm for depth cameras in piece-wise planar environments is presented. It shows that by using surface normal vectors as an input, planar modes in the corresponding density distribution function can be discovered and continuously tracked using efficient non-parametric estimation techniques. The relative rotation can be estimated by registering entire bundles of planar modes by using robust L1-norm minimization. Third, an efficient alternative to the iterative closest point algorithm for real-time tracking of modern depth cameras in ManhattanWorlds is developed. We exploit the common orthogonal structure of man-made environments in order to decouple the estimation of the rotation and the three degrees of freedom of the translation. The derived camera orientation is absolute and thus free of long-term drift, which in turn benefits the accuracy of the translation estimation as well. Fourth, we look into a more general structural regularity—edges. A real-time VO system that uses Canny edges is proposed for RGB-D cameras. Two novel alternatives to classical distance transforms are developed with great properties that significantly improve the classical Euclidean distance field based methods in terms of efficiency, accuracy and robustness. Finally, to deal with challenging scenarios that go beyond what standard RGB/RGB-D cameras can handle, we investigate the recently emerging event camera and focus on the problem of 3D reconstruction from data captured by a stereo event-camera rig moving in a static scene, such as in the context of stereo Simultaneous Localization and Mapping

Image-Aided Navigation Using Cooperative Binocular Stereopsis

This thesis proposes a novel method for cooperatively estimating the positions of two vehicles in a global reference frame based on synchronized image and inertial information. The proposed technique - cooperative binocular stereopsis - leverages the ability of one vehicle to reliably localize itself relative to the other vehicle using image data which enables motion estimation from tracking the three dimensional positions of common features. Unlike popular simultaneous localization and mapping (SLAM) techniques, the method proposed in this work does not require that the positions of features be carried forward in memory. Instead, the optimal vehicle motion over a single time interval is estimated from the positions of common features using a modified bundle adjustment algorithm and is used as a measurement in a delayed state extended Kalman filter (EKF). The developed system achieves improved motion estimation as compared to previous work and is a potential alternative to map-based SLAM algorithms

SVO: Fast Semi-Direct Monocular Visual Odometry

We propose a semi-direct monocular visual odometry algorithm that is precise, robust, and faster than current state-of-the-art methods. The semi-direct approach eliminates the need of costly feature extraction and robust matching techniques for motion estimation. Our algorithm operates directly on pixel intensities, which results in subpixel precision at high frame-rates. A probabilistic mapping method that explicitly models outlier measurements is used to estimate 3D points, which results in fewer outliers and more reliable points. Precise and high frame-rate motion estimation brings increased robustness in scenes of little, repetitive, and high-frequency texture. The algorithm is applied to micro-aerial-vehicle state-estimation in GPS-denied environments and runs at 55 frames per second on the onboard embedded computer and at more than 300 frames per second on a consumer laptop. We call our approach SVO (Semi-direct Visual Odometry) and release our implementation as open-source software

Visual SLAM from image sequences acquired by unmanned aerial vehicles

This thesis shows that Kalman filter based approaches are sufficient for the task of simultaneous localization and mapping from image sequences acquired by unmanned aerial vehicles. Using solely direction measurements to solve the problem of simultaneous localization and mapping (SLAM) is an important part of autonomous systems. Because the need for real-time capable systems, recursive estimation techniques, Kalman filter based approaches are the main focus of interest. Unfortunately, the non-linearity of the triangulation using the direction measurements cause decrease of accuracy and consistency of the results. The first contribution of this work is a general derivation of the recursive update of the Kalman filter. This derivation is based on implicit measurement equations, having the classical iterative non-linear as well as the non-iterative and linear Kalman filter as specializations of our general derivation. Second, a new formulation of linear-motion models for the single camera state model and the sliding window camera state model are given, that make it possible to compute the prediction in a fully linear manner. The third major contribution is a novel method for the initialization of new object points in the Kalman filter. Empirical studies using synthetic and real data of an image sequence of a photogrammetric strip are made, that demonstrate and compare the influences of the initialization methods of new object points in the Kalman filter. Forth, the accuracy potential of monoscopic image sequences from unmanned aerial vehicles for autonomous localization and mapping is theoretically analyzed, which can be used for planning purposes.Visuelle gleichzeitige Lokalisierung und Kartierung aus Bildfolgen von unbemannten Flugkörpern Diese Arbeit zeigt, dass die Kalmanfilter basierte Lösung der Triangulation zur Lokalisierung und Kartierung aus Bildfolgen von unbemannten Flugkörpern realisierbar ist. Aufgrund von Echtzeitanforderungen autonomer Systeme erreichen rekursive Schätz-verfahren, insbesondere Kalmanfilter basierte Ansätze, große Beliebheit. Bedauerlicherweise treten dabei durch die Nichtlinearität der Triangulation einige Effekte auf, welche die Konsistenz und Genauigkeit der Lösung hinsichtlich der geschätzten Parameter maßgeblich beeinflussen. Der erste Beitrag dieser Arbeit besteht in der Herleitung eines generellen Verfahrens zum rekursiven Verbessern im Kalmanfilter mit impliziten Beobachtungsgleichungen. Wir zeigen, dass die klassischen Verfahren im Kalmanfilter eine Spezialisierung unseres Ansatzes darstellen. Im zweiten Beitrag erweitern wir die klassische Modellierung für ein Einkameramodell zu einem Mehrkameramodell im Kalmanfilter. Diese Erweiterung erlaubt es uns, die Prädiktion für eine lineares Bewegungsmodell vollkommen linear zu berechnen. In einem dritten Hauptbeitrag stellen wir ein neues Verfahren zur Initialisierung von Neupunkten im Kalmanfilter vor. Anhand von empirischen Untersuchungen unter Verwendung simulierter und realer Daten einer Bildfolge eines photogrammetrischen Streifens zeigen und vergleichen wir, welchen Einfluß die Initialisierungsmethoden für Neupunkte im Kalmanfilter haben und welche Genauigkeiten für diese Szenarien erreichbar sind. Am Beispiel von Bildfolgen eines unbemannten Flugkörpern zeigen wir in dieser Arbeit als vierten Beitrag, welche Genauigkeit zur Lokalisierung und Kartierung durch Triangulation möglich ist. Diese theoretische Analyse kann wiederum zu Planungszwecken verwendet werden

Grouping Uncertain Oriented Projective Geometric Entities with Application to Automatic Building Reconstruction

The fully automatic reconstruction of 3d scenes from a set of 2d images has always been a key issue in photogrammetry and computer vision and has not been solved satisfactory so far. Most of the current approaches match features between the images based on radiometric cues followed by a reconstruction using the image geometry. The motivation for this work is the conjecture that in the presence of highly redundant data it should be possible to recover the scene structure by grouping together geometric primitives in a bottom-up manner. Oriented projective geometry will be used throughout this work, which allows to represent geometric primitives, such as points, lines and planes in 2d and 3d space as well as projective cameras, together with their uncertainty. The first major contribution of the work is the use of uncertain oriented projective geometry, rather than uncertain projective geometry, that enables the representation of more complex compound entities, such as line segments and polygons in 2d and 3d space as well as 2d edgels and 3d facets. Within the uncertain oriented projective framework a procedure is developed, which allows to test pairwise relations between the various uncertain oriented projective entities. Again, the novelty lies in the possibility to check relations between the novel compound entities. The second major contribution of the work is the development of a data structure, specifically designed to enable performing the tests between large numbers of entities in an efficient manner. Being able to efficiently test relations between the geometric entities, a framework for grouping those entities together is developed. Various different grouping methods are discussed. The third major contribution of this work is the development of a novel grouping method that by analyzing the entropy change incurred by incrementally adding observations into an estimation is able to balance efficiency against robustness in order to achieve better grouping results. Finally the applicability of the proposed representations, tests and grouping methods for the task of purely geometry based building reconstruction from oriented aerial images is demonstrated. It will be shown that in the presence of highly redundant datasets it is possible to achieve reasonable reconstruction results by grouping together geometric primitives.Gruppierung unsicherer orientierter projektiver geometrischer Elemente mit Anwendung in der automatischen Gebäuderekonstruktion Die vollautomatische Rekonstruktion von 3D Szenen aus einer Menge von 2D Bildern war immer ein Hauptanliegen in der Photogrammetrie und Computer Vision und wurde bisher noch nicht zufriedenstellend gelöst. Die meisten aktuellen Ansätze ordnen Merkmale zwischen den Bildern basierend auf radiometrischen Eigenschaften zu. Daran schließt sich dann eine Rekonstruktion auf der Basis der Bildgeometrie an. Die Motivation für diese Arbeit ist die These, dass es möglich sein sollte, die Struktur einer Szene durch Gruppierung geometrischer Primitive zu rekonstruieren, falls die Eingabedaten genügend redundant sind. Orientierte projektive Geometrie wird in dieser Arbeit zur Repräsentation geometrischer Primitive, wie Punkten, Linien und Ebenen in 2D und 3D sowie projektiver Kameras, zusammen mit ihrer Unsicherheit verwendet.Der erste Hauptbeitrag dieser Arbeit ist die Verwendung unsicherer orientierter projektiver Geometrie, anstatt von unsicherer projektiver Geometrie, welche die Repräsentation von komplexeren zusammengesetzten Objekten, wie Liniensegmenten und Polygonen in 2D und 3D sowie 2D Edgels und 3D Facetten, ermöglicht. Innerhalb dieser unsicheren orientierten projektiven Repräsentation wird ein Verfahren zum testen paarweiser Relationen zwischen den verschiedenen unsicheren orientierten projektiven geometrischen Elementen entwickelt. Dabei liegt die Neuheit wieder in der Möglichkeit, Relationen zwischen den neuen zusammengesetzten Elementen zu prüfen. Der zweite Hauptbeitrag dieser Arbeit ist die Entwicklung einer Datenstruktur, welche speziell auf die effiziente Prüfung von solchen Relationen zwischen vielen Elementen ausgelegt ist. Die Möglichkeit zur effizienten Prüfung von Relationen zwischen den geometrischen Elementen erlaubt nun die Entwicklung eines Systems zur Gruppierung dieser Elemente. Verschiedene Gruppierungsmethoden werden vorgestellt. Der dritte Hauptbeitrag dieser Arbeit ist die Entwicklung einer neuen Gruppierungsmethode, die durch die Analyse der änderung der Entropie beim Hinzufügen von Beobachtungen in die Schätzung Effizienz und Robustheit gegeneinander ausbalanciert und dadurch bessere Gruppierungsergebnisse erzielt. Zum Schluss wird die Anwendbarkeit der vorgeschlagenen Repräsentationen, Tests und Gruppierungsmethoden für die ausschließlich geometriebasierte Gebäuderekonstruktion aus orientierten Luftbildern demonstriert. Es wird gezeigt, dass unter der Annahme von hoch redundanten Datensätzen vernünftige Rekonstruktionsergebnisse durch Gruppierung von geometrischen Primitiven erzielbar sind

Grouping Uncertain Oriented Projective Geometric Entities with Application to Automatic Building Reconstruction

The fully automatic reconstruction of 3d scenes from a set of 2d images has always been a key issue in photogrammetry and computer vision and has not been solved satisfactory so far. Most of the current approaches match features between the images based on radiometric cues followed by a reconstruction using the image geometry. The motivation for this work is the conjecture that in the presence of highly redundant data it should be possible to recover the scene structure by grouping together geometric primitives in a bottom-up manner. Oriented projective geometry will be used throughout this work, which allows to represent geometric primitives, such as points, lines and planes in 2d and 3d space as well as projective cameras, together with their uncertainty. The first major contribution of the work is the use of uncertain oriented projective geometry, rather than uncertain projective geometry, that enables the representation of more complex compound entities, such as line segments and polygons in 2d and 3d space as well as 2d edgels and 3d facets. Within the uncertain oriented projective framework a procedure is developed, which allows to test pairwise relations between the various uncertain oriented projective entities. Again, the novelty lies in the possibility to check relations between the novel compound entities. The second major contribution of the work is the development of a data structure, specifically designed to enable performing the tests between large numbers of entities in an efficient manner. Being able to efficiently test relations between the geometric entities, a framework for grouping those entities together is developed. Various different grouping methods are discussed. The third major contribution of this work is the development of a novel grouping method that by analyzing the entropy change incurred by incrementally adding observations into an estimation is able to balance efficiency against robustness in order to achieve better grouping results. Finally the applicability of the proposed representations, tests and grouping methods for the task of purely geometry based building reconstruction from oriented aerial images is demonstrated. lt will be shown that in the presence of highly redundant datasets it is possible to achieve reasonable reconstruction results by grouping together geometric primitives.Gruppierung unsicherer orientierter projektiver geometrischer Elemente mit Anwendung in der automatischen Gebäuderekonstruktion Die vollautomatische Rekonstruktion von 3D Szenen aus einer Menge von 2D Bildern war immer ein Hauptanliegen in der Photogrammetrie und Computer Vision und wurde bisher noch nicht zufriedenstellend gelöst. Die meisten aktuellen Ansätze ordnen Merkmale zwischen den Bildern basierend auf radiometrischen Eigenschaften zu. Daran schließt sich dann eine Rekonstruktion auf der Basis der Bildgeometrie an. Die Motivation für diese Arbeit ist die These, dass es möglich sein sollte, die Struktur einer Szene durch Gruppierung geometrischer Primitive zu rekonstruieren, falls die Eingabedaten genügend redundant sind. Orientierte projektive Geometrie wird in dieser Arbeit zur Repräsentation geometrischer Primitive, wie Punkten, Linien und Ebenen in 2D und 3D sowie projektiver Kameras, zusammen mit ihrer Unsicherheit verwendet. Der erste Hauptbeitrag dieser Arbeit ist die Verwendung unsicherer orientierter projektiver Geometrie, anstatt von unsicherer projektiver Geometrie, welche die Repräsentation von komplexeren zusammengesetzten Objekten, wie Liniensegmenten und Polygonen in 2D und 3D sowie 2D Edgels und 3D Facetten, ermöglicht. Innerhalb dieser unsicheren orientierten projektiven Repräsentation wird ein Verfahren zum Testen paarweiser Relationen zwischen den verschiedenen unsicheren orientierten projektiven geometrischen Elementen entwickelt. Dabei liegt die Neuheit wieder in der Möglichkeit, Relationen zwischen den neuen zusammengesetzten Elementen zu prüfen. Der zweite Hauptbeitrag dieser Arbeit ist die Entwicklung einer Datenstruktur, welche speziell auf die effiziente Prüfung von solchen Relationen zwischen vielen Elementen ausgelegt ist. Die Möglichkeit zur effizienten Prüfung von Relationen zwischen den geometrischen Elementen erlaubt nun die Entwicklung eines Systems zur Gruppierung dieser Elemente. Verschiedene Gruppierungsmethoden werden vorgestellt. Der dritte Hauptbeitrag dieser Arbeit ist die Entwicklung einer neuen Gruppierungsmethode, die durch die Analyse der Änderung der Entropie beim Hinzufügen von Beobachtungen in die Schätzung Effizienz und Robustheit gegeneinander ausbalanciert und dadurch bessere Gruppierungsergebnisse erzielt. Zum Schluss wird die Anwendbarkeit der vorgeschlagenen Repräsentationen, Tests und Gruppierungsmethoden für die ausschließlich geometriebasierte Gebäuderekonstruktion aus orientierten Luftbildern demonstriert. Es wird gezeigt, dass unter der Annahme von hoch redundanten Datensätzen vernünftige Rekonstruktionsergebnisse durch Gruppierung von geometrischen Primitiven erzielbar sind

Plenoptic Signal Processing for Robust Vision in Field Robotics

This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications