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We propose a robust gesture-based communication pipeline for divers toinstruct an Autonomous Underwater Vehicle (AUV) to assist them in performinghigh-risk tasks and helping in case of emergency. A gesture communicationlanguage (CADDIAN) is developed, based on consolidated and standardized divergestures, including an alphabet, syntax and semantics, ensuring a logicalconsistency. A hierarchical classification approach is introduced for handgesture recognition based on stereo imagery and multi-descriptor aggregation tospecifically cope with underwater image artifacts, e.g. light backscatter orcolor attenuation. Once the classification task is finished, a syntax check isperformed to filter out invalid command sequences sent by the diver orgenerated by errors in the classifier. Throughout this process, the diverreceives constant feedback from an underwater tablet to acknowledge or abortthe mission at any time. The objective is to prevent the AUV from executingunnecessary, infeasible or potentially harmful motions. Experimental resultsunder different environmental conditions in archaeological exploration andbridge inspection applications show that the system performs well in the field.

Robust Gesture-Based Communication for Underwater Human-Robot Interaction in the context of Search and Rescue Diver Missions