A Trajectory-Based Approach to Multi-Session Underwater Visual SLAM Using Global Image Signatures
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 matchi...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Conselleria de Salut i Consum del Govern de les Illes Balears |
| Repositorio: | Docusalut |
| Idioma: | inglés |
| OAI Identifier: | oai:docusalut.com:20.500.13003/15576 |
| Acceso en línea: | https://hdl.handle.net/20.500.13003/15576 |
| Access Level: | acceso abierto |
| Palabra clave: | visual SLAM multi-session robot posidonia oceanica |
| Sumario: | 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. |
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