The use of autonomous underwater vehicles (AUVs) to accomplish traditionally challenging and dangerous tasks has proliferated thanks to advances in sensing, navigation, manipulation, and on-board computing technologies. Utilizing AUVs in underwater human-robot interaction (UHRI) has witnessed comparatively smaller levels of growth due to limitations in bi-directional communication and significant technical hurdles to bridge the gap between analogies with terrestrial interaction strategies and those that are possible in the underwater domain. A necessary component to support UHRI is establishing a system for safe robotic-diver approach to establish face-to-face communication that considers non-standard human body pose. In this work, we introduce a stereo vision system for enhancing UHRI that utilizes three-dimensional reconstruction from stereo image pairs and machine learning for localizing human joint estimates. We then establish a convention for a coordinate system that encodes the direction the human is facing with respect to the camera coordinate frame. This allows automatic setpoint computation that preserves human body scale and can be used as input to an image-based visual servo control scheme. We show that our setpoint computations tend to agree both quantitatively and qualitatively with experimental setpoint baselines. The methodology introduced shows promise for enhancing UHRI by improving robotic perception of human orientation underwater.

翻译：随着传感、导航、操控及机载计算技术的进步，自主水下航行器（AUV）在传统挑战性及危险性任务中的应用日益广泛。然而，受限于双向通信的局限性以及将陆地交互策略类比于水下可行方案需跨越的重大技术障碍，AUV在水下人机交互（UHRI）领域的发展相对缓慢。支持UHRI的关键要素之一是建立安全机器人-潜水员接近系统，以实现考虑非标准人体姿态的面对面通信。本研究提出了一种增强UHRI的立体视觉系统，该系统利用立体图像对的三维重建及机器学习进行人体关节估计定位。随后，我们建立了一种编码人体相对于相机坐标系朝向的坐标系统惯用表示法。该方法可自动计算保留人体尺度的设定点，并可作为基于图像的视觉伺服控制方案的输入。研究表明，我们的设定点计算在定量与定性层面均与实验设定点基线保持一致。所提出的方法论通过提升机器人对水下人体朝向的感知能力，展现出增强UHRI的潜力。