In this paper, we propose a method to determine the 3D relative pose of pairs of communicating robots by using human pose-based key-points as correspondences. We adopt a ‘leader-follower’ framework, where at first, the leader robot visually detects and triangulates the key-points using the state-of-the-art pose detector named OpenPose. Afterward, the follower robots match the corresponding 2D projections on their respective calibrated cameras and find their relative poses by solving the perspective-n-point (PnP) problem. In the proposed method, we design an efficient person re-identification technique for associating the mutually visible humans in the scene. Additionally, we present an iterative optimization algorithm to refine the associated key-points based on their local structural properties in the image space. We demonstrate that these refinement processes are essential to establish accurate key-point correspondences across viewpoints. Furthermore, we evaluate the performance of the proposed relative pose estimation system through several experiments conducted in terrestrial and underwater environments. Finally, we discuss the relevant operational challenges of this approach and analyze its feasibility for multi-robot cooperative systems in human-dominated social settings and feature-deprived environments such as underwater.

在本文中，我们提出了一种通过使用基于人体姿势的关键点作为对应关系来确定成对通信机器人的 3D 相对姿势的方法。我们采用“领导者-跟随者”框架，首先，领导者机器人使用名为 OpenPose 的最先进的姿势检测器在视觉上检测和三角测量关键点。之后，跟随机器人在各自校准的相机上匹配相应的 2D 投影，并通过解决透视 n 点 (PnP) 问题找到它们的相对位姿。在所提出的方法中，我们设计了一种有效的人重新识别技术，用于关联场景中相互可见的人。此外，我们提出了一种迭代优化算法，以根据图像空间中的局部结构特性来细化相关的关键点。我们证明了这些细化过程对于建立跨视点的准确关键点对应关系至关重要。此外，我们通过在陆地和水下环境中进行的多项实验来评估所提出的相对姿态估计系统的性能。最后，我们讨论了这种方法的相关操作挑战，并分析了其在人类主导的社会环境和水下等缺乏特征的环境中多机器人协作系统的可行性。