基于面板的水深SLAM加权网格划分

Junwoo Jang, Jinwhan Kim
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引用次数: 2

摘要

水深导航通过减少导航漂移误差而无需GPS定位来实现自主水下航行器的长期运行。在没有水深图的情况下,需要同时定位和映射(SLAM)算法,但这会增加计算复杂性和内存需求。基于面板的测深SLAM可以大大减少计算负担。然而,当车辆不属于更新后的面板时,它可能会遭受不正确的更新。本文提出了一种新的更新方法,即加权网格划分,该方法考虑了车辆位置的概率分布,与标准更新方法相比,在地图精度、计算负担和内存使用方面更有效。通过仿真验证了该算法的可行性。
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Weighted Grid Partitioning for Panel-Based Bathymetric SLAM
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.
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