Zoom control to compensate camera translation within a robot egomotion estimation approach

We previously proposed a method to estimate robot egomotion from the deformation of a contour in the images acquired by a robot-mounted camera [2, 1]. The fact that the contour should always be viewed under weak-perspective conditions limits the applicability of the method. In this paper, we overcome this limitation by controlling the zoom so as to compensate for robot translation along the optic axis. Our control entails minimizing an error signal derived directly from image measurements, without requiring any 3D information. Moreover, contrarily to other 2D control approaches, no point correspondences are needed, since a parametric measure of contour deformation suffices. As a further advantage, the error signal is obtained as a byproduct of egomotion estimation and, therefore, it does not introduce any burden in the computation. Experimental results validate this zooming extension to the method. Moreover, robot translations are correctly computed, including those along the optic axis.Peer Reviewe

Monocular object pose computation with the foveal-peripheral camera of the humanoid robot Armar-III

Active contour modelling is useful to fit non-textured objects, and algorithms have been developed to recover the motion of an object and its uncertainty. Here we show that these algorithms can be used also with point features matched in textured objects, and that active contours and point matches complement in a natural way. In the same manner we also show that depth-from-zoom algorithms, developed for zooming cameras, can be exploited also in the foveal-peripheral eye configuration present in the Armar-III humanoid robot.Peer Reviewe

Structure and motion estimation from apparent contours under circular motion

In this paper, we address the problem of recovering structure and motion from the apparent contours of a smooth surface. Fixed image features under circular motion and their relationships with the intrinsic parameters of the camera are exploited to provide a simple parameterization of the fundamental matrix relating any pair of views in the sequence. Such a parameterization allows a trivial initialization of the motion parameters, which all bear physical meaning. It also greatly reduces the dimension of the search space for the optimization problem, which can now be solved using only two epipolar tangents. In contrast to previous methods, the motion estimation algorithm introduced here can cope with incomplete circular motion and more widely spaced images. Existing techniques for model reconstruction from apparent contours are then reviewed and compared. Experiment on real data has been carried out and the 3D model reconstructed from the estimated motion is presented. © 2002 Elsevier Science B.V. All rights reserved.postprin

Robot Egomotion from the Deformation of Active Contours

Traditional sources of information for image-based computer vision algorithms have been points, lines, corners, and recently SIFT features (Lowe, 2004), which seem to represent at present the state of the art in feature definition. Alternatively, the present work explores the possibility of using tracked contours as informative features, especially in applications no

Biological application of Compressed Sensing Tomography in the Scanning Electron Microscope

The three-dimensional tomographic reconstruction of a biological sample, namely collagen fibrils in human dermal tissue, was obtained from a set of projection-images acquired in the Scanning Electron Microscope. A tailored strategy for the transmission imaging mode was implemented in the microscope and proved effective in acquiring the projections needed for the tomographic reconstruction. Suitable projection alignment and Compressed Sensing formulation were used to overcome the limitations arising from the experimental acquisition strategy and to improve the reconstruction of the sample. The undetermined problem of structure reconstruction from a set of projections, limited in number and angular range, was indeed supported by exploiting the sparsity of the object projected in the electron microscopy images. In particular, the proposed system was able to preserve the reconstruction accuracy even in presence of a significant reduction of experimental projections

Quantitative 3d reconstruction from scanning electron microscope images based on affine camera models

Scanning electron microscopes (SEMs) are versatile imaging devices for the micro-and nanoscale that find application in various disciplines such as the characterization of biological, mineral or mechanical specimen. Even though the specimen’s two-dimensional (2D) properties are provided by the acquired images, detailed morphological characterizations require knowledge about the three-dimensional (3D) surface structure. To overcome this limitation, a reconstruction routine is presented that allows the quantitative depth reconstruction from SEM image sequences. Based on the SEM’s imaging properties that can be well described by an affine camera, the proposed algorithms rely on the use of affine epipolar geometry, self-calibration via factorization and triangulation from dense correspondences. To yield the highest robustness and accuracy, different sub-models of the affine camera are applied to the SEM images and the obtained results are directly compared to confocal laser scanning microscope (CLSM) measurements to identify the ideal parametrization and underlying algorithms. To solve the rectification problem for stereo-pair images of an affine camera so that dense matching algorithms can be applied, existing approaches are adapted and extended to further enhance the yielded results. The evaluations of this study allow to specify the applicability of the affine camera models to SEM images and what accuracies can be expected for reconstruction routines based on self-calibration and dense matching algorithms. © MDPI AG. All rights reserved

Multi-view geometry for general camera models

We consider the structure from motion problem for a previously introduced, highly general imaging model, where cameras are modeled as possibly unconstrained sets of projection rays. This allows to describe most existing camera types (at least for those operating in the visible domain), including pinhole cameras, sensors with radial or more general distortions, catadioptric cameras (central or non-central), etc. We introduce a hierarchy of general camera models: the most general model has unconstrained projection rays whereas the most constrained model dealt with here is the central model, where all rays pass through a single point. Intermediate models are what we call axial cameras (all rays touch a single line), and x-slit cameras (rays touch two lines). The foundations for a multi-view geometry of completely non-central cameras are given, leading to the formulation of multi-view matching tensors, analogous to the fundamental/essential matrices, trifocal and quadrifocal tensors of perspective cameras. This framework is then specialized explicitly for the two-view case, for the intermediate camera types mentioned above.Nous considérons le problème de l’estimation de la structure et du mouvement pour un modèle de caméras hautement général, qui représente une caméra par un ensemble de rayons de projection. Ceci permet de décrire la plupart des types de caméras existants (du moins celles qui opèrent dans le domaine visible), y inclus les caméras sténopé, les caméras avec des distorsions radiales ou plus générales, les caméras catadioptriques (à point de vue unique ou non), etc. Nous introduisons une hiérarchie de modèles de caméras généraux : le modèle le plus général peut posséder des rayons de projection quelconques tandis que le modèle le plus contraint que nous considérons ici est le modèle à point de vue unique (tous les rayons passent par un même point). Parmi les modèles intermédiaires, nous identifions ce que nous appelons les caméras axiales (tous les rayons touchent une même ligne) et les caméras connues sous le nom de « cross-slit » (les rayons touchent deux lignes). Les fondements d’une géométrie d’images multiples pour le modèle de caméras le plus général sont donnés. Ils se manifestent par la formulation de tenseurs d’appariement multi-vues, qui sont l’analogue des matrices fondamentales/essentielles, tenseurs trifocaux ou quadrifocaux des caméras perspectives. Ce cadre théorique général est ensuite spécialisé pour les modèles de caméras intermédiaires mentionnés, pour le cas de deux images

A Multi-view Camera Model for Line-Scan Cameras with Telecentric Lenses

We propose a novel multi-view camera model for line-scan cameras with telecentric lenses. The camera model supports an arbitrary number of cameras and assumes a linear relative motion with constant velocity between the cameras and the object. We distinguish two motion configurations. In the first configuration, all cameras move with independent motion vectors. In the second configuration, the cameras are mounted rigidly with respect to each other and therefore share a common motion vector. The camera model can model arbitrary lens distortions by supporting arbitrary positions of the line sensor with respect to the optical axis. We propose an algorithm to calibrate a multi-view telecentric line-scan camera setup. To facilitate a 3D reconstruction, we prove that an image pair acquired with two telecentric line-scan cameras can always be rectified to the epipolar standard configuration, in contrast to line-scan cameras with entocentric lenses, for which this is possible only under very restricted conditions. The rectification allows an arbitrary stereo algorithm to be used to calculate disparity images. We propose an efficient algorithm to compute 3D coordinates from these disparities. Experiments on real images show the validity of the proposed multi-view telecentric line-scan camera model

Sparse Bayesian information filters for localization and mapping

Submitted 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 February 2008This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment. We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates. This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning. This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior. We demonstrate our filter alongside the SEIF and the standard EKF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the original Gaussian distribution as produced by the EKF, but at much less computational expense. The thesis concludes with an extension of our SLAM filter to a complex underwater environment. We describe a systems-level framework for localization and mapping relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped with a forward-looking sonar. The approach utilizes our filter to fuse measurements of vehicle attitude and motion from onboard sensors with data from sonar images of the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a ship hull