Collaborative 6DoF Relative Pose Estimation for two UAVs with Overlapping Fields of View

Driven by the promise of leveraging the benefits of collaborative robot operation, this paper presents an approach to estimate the relative transformation between two small Unmanned Aerial Vehicles (UAVs), each equipped with a single camera and an inertial sensor, comprising the first step of any meaningful collaboration. Formation flying and collaborative object manipulation are some of the few tasks that the proposed work has direct applications on, while forming a variable-baseline stereo rig using two UAVs carrying a monocular camera each promises unprecedented effectiveness in collaborative scene estimation. Assuming an overlap in the UAVs' fields of view, in the proposed framework, each UAV runs monocular-inertial odometry onboard, while an Extended Kalman Filter fuses the UAVs' estimates and common image measurements to estimate the metrically scaled relative transformation between them, in real-time. Decoupling the direction of the baseline between the cameras of the two UAVs from its magnitude, this work enables consistent and robust estimation of the uncertainty of the relative pose estimation. Our evaluation on both on simulated data and benchmarking datasets consisting of real aerial data, reveals the power of the proposed methodology in a variety of scenarios.