Weighted Grid Partitioning for Panel-Based Bathymetric SLAM

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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.
Publisher
IEEE
Issue Date
2019-06-18
Language
English
Citation

OCEANS 2019 - Marseille

DOI
10.1109/oceanse.2019.8867531
URI
http://hdl.handle.net/10203/271500
Appears in Collection
ME-Conference Papers(학술회의논문)
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