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Author: Sean Augenstein
Publisher: Stanford University
ISBN:
Category :
Languages : en
Pages : 125
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Book Description
This thesis describes the design and implementation of an algorithm for tracking a moving (e.g., `tumbling') target. No a priori information about the target is assumed, and only a single camera is used. The motivation is to enable autonomous rendezvous, inspection, and docking by robots in remote environments, such as space and underwater. Tracking refers to the simultaneous estimation of both the target's 6DOF pose and 3D shape (in the form of a point cloud of recognizable features), a problem of the SLAM (`Simultaneous Localization and Mapping') and SFM (`Structure from Motion') research fields. This research extends SLAM/SFM to deal with non-communicative moving targets (rigid bodies) with unknown, arbitrary 6DOF motion and no a priori knowledge of mass properties, dynamics, shape, or appearance. Specifically, a hybrid algorithm for real-time frame-to-frame pose estimation and shape reconstruction is presented. The algorithm combines concepts from two existing approaches to pose tracking, Bayesian estimation methods and nonlinear optimization techniques, to achieve a real-time capable, feasible, smooth estimate of the relative pose between a robotic platform and a moving target. The rationale for a hybrid approach is explained, and an algorithm is presented. A specific implementation using a modified Rao-Blackwellized particle filter is described and tested. Field demonstrations were performed in conjunction with the Monterey Bay Aquarium Research Institute, using the camera-equipped Remotely Operated Vehicle (ROV) Ventana to observe, reconstruct, and track the pose of an underwater tethered target in Monterey Bay. Results are included which demonstrate the performance and viability of the hybrid approach.
Author: Sean Augenstein
Publisher: Stanford University
ISBN:
Category :
Languages : en
Pages : 125
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Book Description
This thesis describes the design and implementation of an algorithm for tracking a moving (e.g., `tumbling') target. No a priori information about the target is assumed, and only a single camera is used. The motivation is to enable autonomous rendezvous, inspection, and docking by robots in remote environments, such as space and underwater. Tracking refers to the simultaneous estimation of both the target's 6DOF pose and 3D shape (in the form of a point cloud of recognizable features), a problem of the SLAM (`Simultaneous Localization and Mapping') and SFM (`Structure from Motion') research fields. This research extends SLAM/SFM to deal with non-communicative moving targets (rigid bodies) with unknown, arbitrary 6DOF motion and no a priori knowledge of mass properties, dynamics, shape, or appearance. Specifically, a hybrid algorithm for real-time frame-to-frame pose estimation and shape reconstruction is presented. The algorithm combines concepts from two existing approaches to pose tracking, Bayesian estimation methods and nonlinear optimization techniques, to achieve a real-time capable, feasible, smooth estimate of the relative pose between a robotic platform and a moving target. The rationale for a hybrid approach is explained, and an algorithm is presented. A specific implementation using a modified Rao-Blackwellized particle filter is described and tested. Field demonstrations were performed in conjunction with the Monterey Bay Aquarium Research Institute, using the camera-equipped Remotely Operated Vehicle (ROV) Ventana to observe, reconstruct, and track the pose of an underwater tethered target in Monterey Bay. Results are included which demonstrate the performance and viability of the hybrid approach.
Author: Joao Miguel Sanches
Publisher: Springer
ISBN: 3642386288
Category : Computers
Languages : en
Pages : 919
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Book Description
This book constitutes the refereed proceedings of the 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, held in Funchal, Madeira, Portugal, in June 2013. The 105 papers (37 oral and 68 poster ones) presented were carefully reviewed and selected from 181 submissions. The papers are organized in topical sections on computer vision, pattern recognition, image and signal, applications.
Author: Antoine Petit
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this thesis, we address the issue of fully localizing a known object through computer vision, using a monocular camera, what is a central problem in robotics. A particular attention is here paid on space robotics applications, with the aims of providing a unified visual localization system for autonomous navigation purposes for space rendezvous and proximity operations. Two main challenges of the problem are tackled: initially detecting the targeted object and then tracking it frame-by-frame, providing the complete pose between the camera and the object, knowing the 3D CAD model of the object. For detection, the pose estimation process is based on the segmentation of the moving object and on an efficient probabilistic edge-based matching and alignment procedure of a set of synthetic views of the object with a sequence of initial images. For the tracking phase, pose estimation is handled through a 3D model-based tracking algorithm, for which we propose three different types of visual features, pertinently representing the object with its edges, its silhouette and with a set of interest points. The reliability of the localization process is evaluated by propagating the uncertainty from the errors of the visual features. This uncertainty besides feeds a linear Kalman filter on the camera velocity parameters. Qualitative and quantitative experiments have been performed on various synthetic and real data, with challenging imaging conditions, showing the efficiency and the benefits of the different contributions, and their compliance with space rendezvous applications.
Author: Mao Ye
Publisher:
ISBN:
Category :
Languages : en
Pages : 152
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Book Description
Author: Sumant Sharma
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This dissertation addresses the design and validation of new pose estimation algorithms for spaceborne vision-based navigation in close proximity to known uncooperative spacecraft. The onboard estimation of the pose, i.e., relative position and attitude, is a key enabling technology for future on-orbit servicing and debris removal missions. The use of vision-based sensors for pose estimation is particularly attractive due to their low volumetric and power requirements, particularly in comparison to sensors such as LiDAR. Past demonstrations of this technology have relied on various combinations of cooperative use of fiducial markers on the target spacecraft, frequent inputs from the ground control stations, and post-processing of the images on-ground. This research overcomes these limitations by developing novel pose estimation methods that take as input a single two-dimensional image and a simple three-dimensional (3D) wireframe model of the target. These methods estimate the pose without requiring any a-priori pose information or long initialization phases. The first pose estimation method relies on a novel feature detection algorithm based on the filtering of the weak image gradients to identify the edges of the target spacecraft in the image, even in the presence of the Earth in the background. As compared with state-of-the-art feature detection-based methods, this method is shown to be more accurate and computationally faster through experiments on flight imagery. The second pose estimation method leverages modern learning-based algorithms by using a convolutional neural network architecture to classify the pose of the target spacecraft in the image. As compared to the feature detection-based methods, this method is shown to be more robust and two orders of magnitude computationally faster during inference using experiments on synthetic imagery. The third pose estimation method, the Spacecraft Pose Network (SPN), combines the higher accuracy potential of feature detection-based methods with the higher robustness and computational efficiency of learning-based methods. The SPN method achieves this by integrating a convolutional neural network with a Gauss-Newton algorithm. The use of a convolutional neural network allows SPN to implicitly perform feature detection without the need for intensive manual tuning of hyperparameters. The use of a Gauss-Newton algorithm allows the direct application of the underlying physics of the pose estimation problem, i.e., the perspective equations for quantifying the uncertainty in the estimated pose. In contrast to current learning-based methods, the SPN method can be trained using solely synthetic images of a target spacecraft and is shown to generalize its performance on flight imagery of the same target spacecraft. This research also demonstrates that the SPN method can be used for target-in-target pose estimation to handle terminal stages of a docking scenario where only a partial view of the target spacecraft is available. A unique contribution of this research is the generation of the Spacecraft Pose Estimation Dataset (SPEED), which is used to train and evaluate the performance of pose estimation methods. SPEED consists of synthetic images created by fusing OpenGL-based renderings of a spacecraft 3D model with actual meteorological images of the Earth. SPEED also consists of actual camera images created using a seven degrees-of-freedom robotic arm, which positions and orients a vision-based sensor with respect to a full-scale mock-up of a spacecraft. SPEED is being used to host an international competition on pose estimation in collaboration with the European Space Agency. Infinite Orbits is adopting the pose estimation methods developed during this research for onboard deployment during their commercial on-orbit servicing missions.
Author: John Stephen Mullane
Publisher: Springer Science & Business Media
ISBN: 3642213898
Category : Technology & Engineering
Languages : en
Pages : 161
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Book Description
The monograph written by John Mullane, Ba-Ngu Vo, Martin Adams and Ba-Tuong Vo is devoted to the field of autonomous robot systems, which have been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the problem of representing the environment and its uncertainty in terms of feature based maps. Random Finite Sets are adopted as the fundamental tool to represent a map, and a general framework is proposed for feature management, data association and state estimation. The approaches are tested in a number of experiments on both ground based and marine based facilities.
Author: Ronald P. S. Mahler
Publisher: Artech House Publishers
ISBN:
Category : Mathematics
Languages : en
Pages : 892
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Book Description
This comprehensive resource provides you with an in-depth understanding of finite-set statistics (FISST) ndash; a recently developed method which unifies much of information fusion under a single probabilistic, in fact Bayesian, paradigm. The book helps you master FISST concepts, techniques, and algorithms, so you can use FISST to address real-world challenges in the field. You learn how to model, fuse, and process highly disparate information sources, and detect and track non-cooperative individual/platform groups and conventional non-cooperative targets.
Author: Wigbert Fehse
Publisher: Cambridge University Press
ISBN: 1139440683
Category : Technology & Engineering
Languages : en
Pages : 517
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Book Description
The definitive reference for space engineers on rendezvous and docking/berthing (RVD/B) related issues, this book answers key questions such as: How does the docking vehicle accurately approach the target spacecraft? What technology is needed aboard the spacecraft to perform automatic rendezvous and docking, and what systems are required by ground control to supervise this process? How can the proper functioning of all rendezvous-related equipment, systems and operations be verified before launch? The book provides an overview of the major issues governing approach and mating strategies, and system concepts for rendezvous and docking/berthing. These issues are described and explained such that aerospace engineers, students and even newcomers to the field can acquire a basic understanding of RVD/B. The author would like to extend his thanks to Dr Shufan Wu, GNC specialist and translator of the book's Chinese edition, for his help in the compilation of these important errata.
Author: Courtney G. Brooks
Publisher: Courier Corporation
ISBN: 0486140938
Category : Science
Languages : en
Pages : 578
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Book Description
This illustrated history by a trio of experts is the definitive reference on the Apollo spacecraft and lunar modules. It traces the vehicles' design, development, and operation in space. More than 100 photographs and illustrations.
Author: Simone D′Amico
Publisher: Wiley-Blackwell
ISBN: 9781119808954
Category :
Languages : en
Pages : 500
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Book Description
