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• W2013117623 abstract "This paper reports on a method for an autonomous underwater vehicle to perform real-time visual simultaneous localization and mapping (SLAM) on large ship hulls over multiple sessions. Along with a monocular camera, our method uses a piecewise-planar model to explicitly optimize the ship hull surface in our factor-graph framework, and anchor nodes to co-register multiple surveys. To enable realtime performance for long-term SLAM, we use the recent Generic Linear Constraints (GLC) framework to sparsify our factor-graph. This paper analyzes how our single-session SLAM techniques can be used in the GLC framework, and describes a particle filter reacquisition algorithm so that an underwater session can be automatically re-localized to a previously built SLAM graph. We provide real-world experimental results involving automated ship hull inspection, and show that our localization filter out-performs Fast Appearance-Based Mapping (FAB-MAP), a popular place-recognition system. Using our approach, we can automatically align surveys that were taken days, months, and even years apart." @default.
• W2013117623 created "2016-06-24" @default.
• W2013117623 creator A5020864898 @default.
• W2013117623 creator A5075629304 @default.
• W2013117623 date "2014-05-01" @default.
• W2013117623 modified "2023-09-27" @default.
• W2013117623 title "Toward long-term, automated ship hull inspection with visual SLAM, explicit surface optimization, and generic graph-sparsification" @default.
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• W2013117623 doi "https://doi.org/10.1109/icra.2014.6907415" @default.
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