20,601 research outputs found

    Hierarchical structure-and-motion recovery from uncalibrated images

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    This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D struc- ture from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.Comment: Accepted for publication in CVI

    Estimation of Motion from a Sequence of Images Using Spherical Projective Geometry

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    Motion is an important cue for many applications. Here we propose a solution for estimating motion from a sequence of images using three algorithms, viz., Batch, Recursive and Bootstrap methods. The motion derived using spherical projection relates the image motion to the object motion. This equation is reformulated into a dynamical space state model, for which Kalman filter can be easily applied to yield the estimate of depth. We also propose a new approach for establishing correspondences using local planar invariants and hierarchical groupings. The proposed algorithm provides a simple yet robust method having lower time complexity and less ambiguity in matching than its predecessors

    Robust Camera Location Estimation by Convex Programming

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    33D structure recovery from a collection of 22D images requires the estimation of the camera locations and orientations, i.e. the camera motion. For large, irregular collections of images, existing methods for the location estimation part, which can be formulated as the inverse problem of estimating nn locations t1,t2,…,tn\mathbf{t}_1, \mathbf{t}_2, \ldots, \mathbf{t}_n in R3\mathbb{R}^3 from noisy measurements of a subset of the pairwise directions ti−tj∥ti−tj∥\frac{\mathbf{t}_i - \mathbf{t}_j}{\|\mathbf{t}_i - \mathbf{t}_j\|}, are sensitive to outliers in direction measurements. In this paper, we firstly provide a complete characterization of well-posed instances of the location estimation problem, by presenting its relation to the existing theory of parallel rigidity. For robust estimation of camera locations, we introduce a two-step approach, comprised of a pairwise direction estimation method robust to outliers in point correspondences between image pairs, and a convex program to maintain robustness to outlier directions. In the presence of partially corrupted measurements, we empirically demonstrate that our convex formulation can even recover the locations exactly. Lastly, we demonstrate the utility of our formulations through experiments on Internet photo collections.Comment: 10 pages, 6 figures, 3 table

    Stable Camera Motion Estimation Using Convex Programming

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    We study the inverse problem of estimating n locations t1,...,tnt_1, ..., t_n (up to global scale, translation and negation) in RdR^d from noisy measurements of a subset of the (unsigned) pairwise lines that connect them, that is, from noisy measurements of ±(ti−tj)/∥ti−tj∥\pm (t_i - t_j)/\|t_i - t_j\| for some pairs (i,j) (where the signs are unknown). This problem is at the core of the structure from motion (SfM) problem in computer vision, where the tit_i's represent camera locations in R3R^3. The noiseless version of the problem, with exact line measurements, has been considered previously under the general title of parallel rigidity theory, mainly in order to characterize the conditions for unique realization of locations. For noisy pairwise line measurements, current methods tend to produce spurious solutions that are clustered around a few locations. This sensitivity of the location estimates is a well-known problem in SfM, especially for large, irregular collections of images. In this paper we introduce a semidefinite programming (SDP) formulation, specially tailored to overcome the clustering phenomenon. We further identify the implications of parallel rigidity theory for the location estimation problem to be well-posed, and prove exact (in the noiseless case) and stable location recovery results. We also formulate an alternating direction method to solve the resulting semidefinite program, and provide a distributed version of our formulation for large numbers of locations. Specifically for the camera location estimation problem, we formulate a pairwise line estimation method based on robust camera orientation and subspace estimation. Lastly, we demonstrate the utility of our algorithm through experiments on real images.Comment: 40 pages, 12 figures, 6 tables; notation and some unclear parts updated, some typos correcte

    Using Virtual Reality to increase technical performance during rowing workouts

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    Technology is advancing rapidly in virtual reality (VR) and sensors, gathering feedback from our body and the environment we are interacting in. Combining the two technologies gives us the opportunity to create personalized and reactive immersive environments. These environments can be used e.g. for training in dangerous situations (e.g. fire, crashes, etc), or to improve skills with less distraction than regular natural environments would have. The pilot study described in this thesis puts an athlete who is rowing on a stationary rowing machine into a virtual environment. The VR takes movement from several sensors of the ergo-meter and displays those in VR. In addition, metrics on technique are being derived from the sensor data and physiological data. All this is used to investigate if, and to which extent, VR may improve the technical skills of the athlete during the complex sport of rowing. Furthermore, athletes are giving subjective feedback about their experience comparing a standard rowing workout, with the workout using VR. First results indicate better performance and an enhanced experience by the athlete

    Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

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    Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved
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