VI-RPE: Visual-Inertial Relative Pose Estimation for Aerial Vehicles

With a large body of literature dedicated to ego-motion estimation and perception of a robot's workspace, the robotics community has seen some impressive advances in self-localization and mapping, however, we are still far from general applicability of such approaches in real scenarios. Driven by the need for portable and low-cost solutions to relative pose estimation between unmanned aerial vehicles (UAVs), in this letter, we propose a new framework to track a master UAV in real-time, carrying a known constellation of LED markers, from a slave UAV without any other pose estimation capability. This setup is especially interesting to aerial manipulation and close-up inspection of structures with low or no texture. Our approach is able to fuse the estimated master's pose with the slave's onboard inertial readings, supporting intermittent communication between the UAVs. Evaluation on both simulation and real indoor and outdoor experiments reveals that the proposed approach achieves unprecedented robustness to noise and occlusion, accuracy, and speed of computation. All the code to reproduce this work is publicly available.