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oai:repisalud.isciii.es:20.500.12105/227452024-11-28T23:41:08Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967

00925njm 22002777a 4500 dc Burguera, Antoni author Bonin-Font, Francisco author 2019-08 This paper presents a multi-session monocular Simultaneous Localization and Mapping (SLAM) approach focused on underwater environments. The system is composed of three main blocks: a visual odometer, a loop detector, and an optimizer. Single session loop closings are found by means of feature matching and Random Sample Consensus (RANSAC) within a search region. Multi-session loop closings are found by comparing hash-based global image signatures. The optimizer refines the trajectories and joins the different maps. Map joining preserves the trajectory structure by adding a single link between the joined sessions, making it possible to aggregate or disaggregate sessions whenever is necessary. All the optimization processes can be delayed until a certain number of loops has been found in order to reduce the computational cost. Experiments conducted in real subsea scenarios show the quality and robustness of this proposal. Burguera A, Bonnín Pascual F. A Trajectory-Based Approach to Multi-Session Underwater Visual SLAM Using Global Image Signatures. J Mar Sci Eng. 2019 Aug;7(8):278. http://hdl.handle.net/20.500.13003/15576 https://hdl.handle.net/20.500.12105/22745 10.3390/jmse7080278 2077-1312 Journal of Marine Science and Engineering 2-s2.0-85071173012 482991100008 Visual SLAM Multi-session robot Posidonia oceanica A Trajectory-Based Approach to Multi-Session Underwater Visual SLAM Using Global Image Signatures