This paper presents the design of a monocular vision based particle filter localization system for urban settings that uses aerial orthoimagery as the reference map. One of the design objectives is to provide a low cost method for outdoor localization using a single camera. This relaxes the need for global positioning system (GPS) which may experience degraded reliability in urban settings. The second objective is to study the achievable localization performance with the aforementioned resources. Image processing techniques are employed to create a feature map from an aerial image, and also to extract features from camera images to provide observations that are used by a particle filter for localization.

/pdf/monocular-vision-based-particle-filter-localization-in-urban-4kazxzxyqg.pdf

Monocular Vision based Particle Filter Localization in Urban Environments

In this work, we intend to go one step further to overcome the difficulty that lies in the gap between low-level media features (e.g. colors, texture, motion, etc.) and high-level concepts to perform a reliable content-based indexing and retrieval. More especially, our work proposes a new way to establish a connection between both geometric and radiometric deformations and the characterization of them in terms of camera operations. Based on both the apparent motion and the defocus blur (low-level features), we estimate extrinsic and intrinsic camera parameter changes, and then deduce 3D camera operations (i.e. mid-level features), such as panning/tracking, tilting/booming, zooming/ dollying and rolling, as well as focus changes. Finally, camera operations are recorded into an index which is then used for video retrieval. Experiments confirm that the proposed mid-level features can be accurately deduced from low-level features and that they can be used for indexing and retrieval purpose.

Interpreting Camera Operations in the Context of Content-based Video Indexing and Retrieval

Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb.d-nb.de abrufbar. Acknowledgements First of all, I would like to thank Prof. Dr. Rolf-Rainer Grigat for giving me the opportunity of doing research in the fascinating field of computer vision and for supporting my work. I am also greatful to Prof. Dr. Reinhard Koch for being the second referee of my thesis and to Prof. Dr. Siegfried Rump for chairing my graduation process. The members of the Vision Systems group made this research very enjoyable, and I would like to thank them all for many technical discussions. Special thanks go to Marco Grimm for creative conversations on our ideas, to Philipp Urban who helped me identify mathematical pitfalls, and to Dipl.-Ing. Ralph Kricke for always increasing my mood with his excellent humor. Many thanks go to my friends Thilo Heinrichson, Marko Soltau, Rüdiger Bölcke and Matthias Dehn who listened to me when not everything went fine and who helped me free my mind in life outside the university. I would like to thank my parents deeply for their continuous support. Finally, Sandra, I would like to thank you for your love, for your patience and for always keeping me grounded.

/pdf/robust-relative-pose-estimation-of-two-cameras-by-28jj16n8h3.pdf

Robust Relative Pose Estimation of two Cameras by Decomposing Epipolar Geometry

Camera ego-motion is computed utilizing optical flows which are obtained from sparsely located feature points, where the corresponding points are determined through the information fusion of the correlation and the system model-based prediction. Since the ego-motion of the camera is obtained utilizing optical flows, the accuracy of recognized motion depends highly on the correctness of optical flows. Therefore, the ego-motion utilizing feature-based optical flows are more reliable than those of other points due to the distinctive characteristic of feature points. The technical bottleneck of this category of solution is the matching of corresponding points. In this paper, the correlation and prediction is fused to determine trustable matching pairs. For better prediction, system dynamic model is employed. The effect of the proposed algorithm has been shown through the motion estimation of the camera installed on a dynamic system.

Computation of the real-time camera motion through a correlation and a dynamic model of system

Ego-motion technology holds great significance for computer vision applications, robotics, augmented reality and visual simultaneous localization and mapping. This paper is a study of ego-motion estimation basic concepts, equipment, algorithms, challenges and its real world applications. First, we provide an overview for motion estimation in general with special focus on ego-motion estimation and its basic concepts. For ego-motion estimation it's necessary to understand the notion of independent moving objects, focus of expansion, motion field, and optical flow. Vital algorithms that are used for ego-motion estimation are critically discussed in the following section of the paper. Various camera setups and their potential weakness and strength are also studied in context of ego-motion estimation. We also briefly specify some ego-motion applications used in the real world. We conclude the paper by discussing some open problems, provide some future directions and finally summarize the entire paper in the conclusions.

Ego-motion estimation concepts, algorithms and challenges: an overview

This contribution presents a low-complexity camera egomotion estimation algorithm for real-time applications. The algorithm uses a feature based approach for motion estimation. A new method is introduced for feature selection which limits the number of feature points to be tracked and has a low dependency on structure in the image. Both these factors are important in real time, applications, as lesser features to track result in lower computational complexity and lesser dependency on image structure results in smaller variations in computational time for different images. This gain in speed is achieved at the cost of a slightly reduced robustness and accuracy. This trade-off between speed and accuracy pays off particularly in static scenes where high reduction in computational cost is achieved without the accuracy penalty. This algorithm can be used in applications where an estimate of camera motion is required and low computational complexity is of primary concern.

Low-complexity camera ego-motion estimation algorithm for real time applications

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Low-complexity camera ego-motion estimation algorithm for real time applications