Robot-to-Robot Relative Pose Estimation using Humans as Markers

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 the leader robot detects and triangulates the key-points in its own frame of reference. Afterwards, 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 use the state-of-the-art pose detector named OpenPose for extracting the pose-based key-points pertaining to humans in the scene. Additionally, we design an efficient model for person re-identification and present an iterative optimization algorithm to refine the key-point correspondences based on their local structural similarities in the image space. We evaluate the performance of the proposed relative pose estimation method through a number of 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 in feature-deprived environments such as underwater.