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Recent advances in efficient design, perception algorithms, and computinghardware have made it possible to create improved human-robot interaction (HRI)capabilities for autonomous underwater vehicles (AUVs). To conduct securemissions as underwater human-robot teams, AUVs require the ability toaccurately identify divers. However, this remains an open problem due todivers' challenging visual features, mainly caused by similar-looking scubagear. In this paper, we present a novel algorithm that can perform diveridentification using either pre-trained models or models trained duringdeployment. We exploit anthropometric data obtained from diver pose estimatesto generate robust features that are invariant to changes in distance andphotometric conditions. We also propose an embedding network that maximizesinter-class distances in the feature space and minimizes those for theintra-class features, which significantly improves classification performance.Furthermore, we present an end-to-end diver identification framework thatoperates on an AUV and evaluate the accuracy of the proposed algorithm.Quantitative results in controlled-water experiments show that our algorithmachieves a high level of accuracy in diver identification.

Diver Identification Using Anthropometric Data Ratios for Underwater Multi-Human-Robot Collaboration