Preserving Relative Localization of FoV-Limited Drone Swarm via Active Mutual Observation

TL;DR

This paper introduces an active localization correction system for drone swarms with limited FoV stereo cameras, balancing environment observation and mutual localization to reduce drift and maintain formations in GPS-denied environments.

Contribution

It proposes a yaw planner that actively manages camera orientation to improve relative localization accuracy while ensuring environment observation, validated through simulation and real-world tests.

Findings

Reduced positioning drift by up to 65% in experiments.

Maintained formation in indoor and outdoor GPS-denied flights.

Validated scalability and robustness of the proposed system.

Abstract

Relative state estimation is crucial for vision-based swarms to estimate and compensate for the unavoidable drift of visual odometry. For autonomous drones equipped with the most compact sensor setting -- a stereo camera that provides a limited field of view (FoV), the demand for mutual observation for relative state estimation conflicts with the demand for environment observation. To balance the two demands for FoV limited swarms by acquiring mutual observations with a safety guarantee, this paper proposes an active localization correction system, which plans camera orientations via a yaw planner during the flight. The yaw planner manages the contradiction by calculating suitable timing and yaw angle commands based on the evaluation of localization uncertainty estimated by the Kalman Filter. Simulation validates the scalability of our algorithm. In real-world experiments, we reduce positioning drift by up to 65% and managed to maintain a given formation in both indoor and outdoor GPS-denied flight, from which the accuracy, efficiency, and robustness of the proposed system are verified.