DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Junwoo | ko |
dc.contributor.author | Kim, Jinwhan | ko |
dc.date.accessioned | 2020-01-19T09:20:25Z | - |
dc.date.available | 2020-01-19T09:20:25Z | - |
dc.date.created | 2020-01-13 | - |
dc.date.created | 2020-01-13 | - |
dc.date.issued | 2019-06-18 | - |
dc.identifier.citation | OCEANS - Marseille Conference | - |
dc.identifier.issn | 0197-7385 | - |
dc.identifier.uri | http://hdl.handle.net/10203/271500 | - |
dc.description.abstract | Bathymetric navigation enables the long-term operation of autonomous underwater vehicles by reducing navigation drift errors with no need for GPS position fixes. In the case that a bathymetric map is not available, the simultaneous localization and mapping (SLAM) algorithm is required, but this increases computational complexity and memory requirement. Panel-based bathymetric SLAM could considerably reduce the computational burden. However, it may suffers from incorrect update when the vehicle does not belong to the updated panel. This study proposes a new update method, called weighted grid partitioning, which considers the probability distribution of a vehicle's location, and is more effective in terms of the map accuracy, computational burden, and memory usage compared to standard update methods. The feasibility of the proposed algorithm is verified through simulations. | - |
dc.language | English | - |
dc.publisher | IEEE | - |
dc.title | Weighted Grid Partitioning for Panel-Based Bathymetric SLAM | - |
dc.type | Conference | - |
dc.identifier.wosid | 000591652100462 | - |
dc.identifier.scopusid | 2-s2.0-85088380576 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | OCEANS - Marseille Conference | - |
dc.identifier.conferencecountry | FR | - |
dc.identifier.conferencelocation | Marseille, France | - |
dc.identifier.doi | 10.1109/oceanse.2019.8867531 | - |
dc.contributor.localauthor | Kim, Jinwhan | - |
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