Experimental assessment of image hashing methods for loop closure in multi-robot underwater SLAM, indicating performance differences.
This article presents an experimental assessment of a hash-based loop closure detection methodology specially addressed to Multi-robot underwater visual Simultaneous Localization and Mapping (SLAM). This methodology uses two diferent top quality image global descriptors, one learned (NetVLAD) and one handcrafted (HALOC). Complete tests were done to compare the performance of both hashing techniques applied in an extensive set of real underwater imagery.
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Bonin‐Font et al. (2021) studied this question.
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