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

## TL;DR

This paper introduces a novel method for estimating the 3D relative pose between robots using human pose key-points as markers, leveraging visual detection, re-identification, and iterative refinement in diverse environments.

## Contribution

The paper presents a new approach combining human pose detection, re-identification, and iterative optimization for robot relative pose estimation using humans as markers.

## Key findings

- Effective in terrestrial and underwater environments
- Accurate key-point correspondence improves pose estimation
- Feasible for multi-robot systems in human-centric settings

## Abstract

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.