Egomotion estimation using binocular spatiotemporal oriented energy

Camera egomotion estimation is concerned with the recovery of a camera's motion (e.g., instantaneous translation and rotation) as it moves through its environment. It has been demonstrated to be of both theoretical and practical interest. This thesis documents a novel algorithm for egomotion estimation based on binocularly matched spatiotemporal oriented energy distributions. Basing the estimation on oriented energy measurements makes it possible to recover egomotion without the need to establish temporal correspondences or convert disparity into 3D world coordinates. There sulting algorithm has been realized in software and evaluated quantitatively on a novel laboratory dataset with ground truth as well as qualitatively on both indoor and outdoor real-world datasets. Performance is evaluated relative to comparable alternative algorithms and shown to exhibit best overall performance

Review: Extending Visible Band Computer Vision Techniques to Infrared Band Images

Infared imaging process bears many similarities to the visible imaging process. If visible band computer vision techniques can be used on infrared images with no or small adjustments it would save us the trouble of redeveloping a whole new set of techniques. However, there are important differences in the practical environmental parameters between visible and infrared bands that invalidates many convenient background assumptions inherent to visible-band computer vision techniques. We review here the underlying reasons why some computer vision techniques can while some cannot be applied directly to infrared images. We also examine a few attempts to extend computer vision to infrared images and discuss their relative merits

Large-area visually augmented navigation for autonomous underwater vehicles

Submitted to the Joint Program in Applied Ocean Science & Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2005This thesis describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of autonomous underwater vehicles (AUVs) while exploiting the inertial sensor information that is routinely available on such platforms. We adopt a systems-level approach exploiting the complementary aspects of inertial sensing and visual perception from a calibrated pose-instrumented platform. This systems-level strategy yields a robust solution to underwater imaging that overcomes many of the unique challenges of a marine environment (e.g., unstructured terrain, low-overlap imagery, moving light source). Our large-area SLAM algorithm recursively incorporates relative-pose constraints using a view-based representation that exploits exact sparsity in the Gaussian canonical form. This sparsity allows for efficient O(n) update complexity in the number of images composing the view-based map by utilizing recent multilevel relaxation techniques. We show that our algorithmic formulation is inherently sparse unlike other feature-based canonical SLAM algorithms, which impose sparseness via pruning approximations. In particular, we investigate the sparsification methodology employed by sparse extended information filters (SEIFs) and offer new insight as to why, and how, its approximation can lead to inconsistencies in the estimated state errors. Lastly, we present a novel algorithm for efficiently extracting consistent marginal covariances useful for data association from the information matrix. In summary, this thesis advances the current state-of-the-art in underwater visual navigation by demonstrating end-to-end automatic processing of the largest visually navigated dataset to date using data collected from a survey of the RMS Titanic (path length over 3 km and 3100 m2 of mapped area). This accomplishment embodies the summed contributions of this thesis to several current SLAM research issues including scalability, 6 degree of freedom motion, unstructured environments, and visual perception.This work was funded in part by the CenSSIS ERC of the National Science Foundation under grant EEC-9986821, in part by the Woods Hole Oceanographic Institution through a grant from the Penzance Foundation, and in part by a NDSEG Fellowship awarded through the Department of Defense

Constrained camera motion estimation and 3D reconstruction

The creation of virtual content from visual data is a tedious task which requires a high amount of skill and expertise. Although the majority of consumers is in possession of multiple imaging devices that would enable them to perform this task in principle, the processing techniques and tools are still intended for the use by trained experts. As more and more capable hardware becomes available, there is a growing need among consumers and professionals alike for new flexible and reliable tools that reduce the amount of time and effort required to create high-quality content. This thesis describes advances of the state of the art in three areas of computer vision: camera motion estimation, probabilistic 3D reconstruction, and template fitting. First, a new camera model geared towards stereoscopic input data is introduced, which is subsequently developed into a generalized framework for constrained camera motion estimation. A probabilistic reconstruction method for 3D line segments is then described, which takes global connectivity constraints into account. Finally, a new framework for symmetry-aware template fitting is presented, which allows the creation of high-quality models from low-quality input 3D scans. Evaluations with a broad range of challenging synthetic and real-world data sets demonstrate that the new constrained camera motion estimation methods provide improved accuracy and flexibility, and that the new constrained 3D reconstruction methods improve the current state of the art.Die Erzeugung virtueller Inhalte aus visuellem Datenmaterial ist langwierig und erfordert viel Geschick und Sachkenntnis. Obwohl der Großteil der Konsumenten mehrere Bildgebungsgeräte besitzt, die es ihm im Prinzip erlauben würden, dies durchzuführen, sind die Techniken und Werkzeuge noch immer für den Einsatz durch ausgebildete Fachleute gedacht. Da immer leistungsfähigere Hardware zur Verfügung steht, gibt es sowohl bei Konsumenten als auch bei Fachleuten eine wachsende Nachfrage nach neuen flexiblen und verlässlichen Werkzeugen, die die Erzeugung von qualitativ hochwertigen Inhalten vereinfachen. In der vorliegenden Arbeit werden Erweiterungen des Stands der Technik in den folgenden drei Bereichen der Bildverarbeitung beschrieben: Kamerabewegungsschätzung, wahrscheinlichkeitstheoretische 3D-Rekonstruktion und Template-Fitting. Zuerst wird ein neues Kameramodell vorgestellt, das für die Verarbeitung von stereoskopischen Eingabedaten ausgelegt ist. Dieses Modell wird in der Folge in eine generalisierte Methode zur Kamerabewegungsschätzung unter Nebenbedingungen erweitert. Anschließend wird ein wahrscheinlichkeitstheoretisches Verfahren zur Rekonstruktion von 3D-Liniensegmenten beschrieben, das globale Verbindungen als Nebenbedingungen berücksichtigt. Schließlich wird eine neue Methode zum Fitting eines Template-Modells präsentiert, bei der die Berücksichtigung der Symmetriestruktur des Templates die Erzeugung von Modellen hoher Qualität aus 3D-Eingabedaten niedriger Qualität erlaubt. Evaluierungen mit einem breiten Spektrum an anspruchsvollen synthetischen und realen Datensätzen zeigen, dass die neuen Methoden zur Kamerabewegungsschätzung unter Nebenbedingungen höhere Genauigkeit und mehr Flexibilität ermöglichen, und dass die neuen Methoden zur 3D-Rekonstruktion unter Nebenbedingungen den Stand der Technik erweitern