Dorian Gálvez-López

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—We propose a novel method for visual place recognition using bag of words obtained from FAST+BRIEF features. For the first time, we build a vocabulary tree that discretizes a binary descriptor space, and use the tree to speed up correspondences for geometrical verification. We present competitive results with no false positives in very different datasets,(More)
— We present a method for detecting revisited places in a image sequence in real time by using efficient features. We introduce three important novelties to the bag-of-words plus geometrical checking approach. We use FAST keypoints and BRIEF descriptors, which are binary and very fast to compute (less that 20µs per point). To perform image comparisons, we(More)
— Place recognition is a challenging task in any SLAM system. Algorithms based on visual appearance are becoming popular to detect locations already visited, also known as loop closures, because cameras are easily available and provide rich scene detail. These algorithms typically result in pairs of images considered depicting the same location. To avoid(More)
— We describe a method for an autonomous robot to efficiently locate one or more distinct objects in a realistic environment using monocular vision. We demonstrate how to efficiently subdivide acquired images into interest regions for the robot to zoom in on, using receptive field cooccurrence his-tograms. Objects are recognized through SIFT feature(More)
— Monocular SLAM systems have been mainly fo-cused on producing geometric maps just composed of points or edges; but without any associated meaning or semantic content. In this paper, we propose a semantic SLAM algorithm that merges in the estimated map traditional meaningless points with known objects. The non-annotated map is built using only the(More)
—We propose a place recognition algorithm for simultaneous localization and mapping (SLAM) systems using stereo cameras that considers both appearance and geometric information of points of interest in the images. Both near and far scene points provide information for the recognition process. Hypotheses about loop closings are generated using a fast(More)
—The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work we describe the RoboEarth semantic mapping system. The semantic map is composed of (1) an ontology to code the concepts and relations in maps and objects, and (2) a SLAM map(More)
— The work described in this paper concerns the problem of detecting loop-closure situations whenever an autonomous vehicle returns to previously visited places in the navigation area. An appearance-based perspective is considered by using images gathered by the on-board vision sensors for navigation tasks in heterogeneous environments characterized by the(More)
We present a real-time object-based SLAM system that leverages the largest object database to date. Our approach comprises two main components: 1) a monocular SLAM algorithm that exploits object rigidity constraints to improve the map and find its real scale, and 2) a novel object recognition algorithm based on bags of binary words, which provides live(More)