On Keyframe Positioning for Pose Graphs Applied to Visual SLAM

Abstract : In this work, a new method is introduced for localization and keyframe identification to solve a Simultaneous Localization and Mapping (SLAM) problem. The proposed approach is based on a dense spherical acquisition system that synthesizes spherical intensity and depth images at arbitrary locations. The images are related by a graph of 6 degrees-of-freedom (DOF) poses which are estimated through spherical registration. A direct image-based method is provided to estimate pose by using both depth and color information simultaneously. A new keyframe identification method is proposed to build the map of the environment by using the covariance matrix between raletive 6 DOF poses, which is basically the uncertainty of the estimated pose. This new approach is shown to be more robust than an error-based keyframe identification method. Navigation using the maps built from our method also gives less trajectory error than using maps from other methods.
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Communication dans un congrès
IEEE/RSJ International Conference on Intelligent Robots and Systems, 5th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Nov 2013, Tokyo, Japan. IEEE/RSJ International Conference on Intelligent Robots and Systems, 5th Workshop on Planning, Perception and Navigation for Intelligent Vehicles 2013
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Dernière modification le : samedi 18 novembre 2017 - 01:15:54

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Andru Putra Twinanda, Maxime Meilland, Désiré Sidibé, Andrew I. Comport. On Keyframe Positioning for Pose Graphs Applied to Visual SLAM. IEEE/RSJ International Conference on Intelligent Robots and Systems, 5th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Nov 2013, Tokyo, Japan. IEEE/RSJ International Conference on Intelligent Robots and Systems, 5th Workshop on Planning, Perception and Navigation for Intelligent Vehicles 2013. 〈hal-01357358〉

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