1,736 research outputs found

    Vision-Based Localization Algorithm Based on Landmark Matching, Triangulation, Reconstruction, and Comparison

    No full text
    Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRC) global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data

    Non-Metrical Navigation Through Visual Path Control

    Get PDF
    We describe a new method for wide-area, non-metrical robot navigationwhich enables useful, purposeful motion indoors. Our method has twophases: a training phase, in which a human user directs a wheeledrobot with an attached camera through an environment while occasionallysupplying textual place names; and a navigation phase in which theuser specifies goal place names (again as text), and the robot issueslow-level motion control in order to move to the specified place. We show thatdifferences in the visual-field locations and scales of features matched acrosstraining and navigation can be used to construct a simple and robust controlrule that guides the robot onto and along the training motion path.Our method uses an omnidirectional camera, requires approximateintrinsic and extrinsic camera calibration, and is capable of effective motioncontrol within an extended, minimally-prepared building environment floorplan.We give results for deployment within a single building floor with 7 rooms, 6corridor segments, and 15 distinct place names

    Graph-Based Classification of Omnidirectional Images

    Get PDF
    Omnidirectional cameras are widely used in such areas as robotics and virtual reality as they provide a wide field of view. Their images are often processed with classical methods, which might unfortunately lead to non-optimal solutions as these methods are designed for planar images that have different geometrical properties than omnidirectional ones. In this paper we study image classification task by taking into account the specific geometry of omnidirectional cameras with graph-based representations. In particular, we extend deep learning architectures to data on graphs; we propose a principled way of graph construction such that convolutional filters respond similarly for the same pattern on different positions of the image regardless of lens distortions. Our experiments show that the proposed method outperforms current techniques for the omnidirectional image classification problem

    A minimalistic approach to appearance-based visual SLAM

    Get PDF
    This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses

    Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

    Get PDF
    This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground
    corecore