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Found 5 papers, 1 papers with code
Robotic Detection of a Human-Comprehensible Gestural Language for Underwater Multi-Human-Robot Collaboration
Our experimental evaluations, both in simulation and in closed-water robot trials, demonstrate that the proposed RRCommNet architecture is able to decipher gesture-based messages with an average accuracy of 88-94% on simulated data, 73-83% on real data (depending on the version of the model used).
An Analysis of Deep Object Detectors For Diver Detection
We evaluate these networks on typical accuracy and efficiency metrics, as well as on the temporal stability of their detections.
TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris
This paper presents TrashCan, a large dataset comprised of images of underwater trash collected from a variety of sources, annotated both using bounding boxes and segmentation labels, for development of robust detectors of marine debris.
A Generative Approach Towards Improved Robotic Detection of Marine Litter
The proposed approach relies on a two-stage variational autoencoder (VAE) and a binary classifier to evaluate the generated imagery for quality and realism.
By Land, Air, or Sea: Multi-Domain Robot Communication Via Motion
In this paper, we explore the use of motion for robot-to-human communication on three robotic platforms: the 5 degrees-of-freedom (DOF) Aqua autonomous underwater vehicle (AUV), a 3-DOF camera gimbal mounted on a Matrice 100 drone, and a 3-DOF Turtlebot2 terrestrial robot.
Robotics
