  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.
