Distributed State Estimation for Vision-Based Cooperative Slung Load Transportation in GPS-Denied Environments

TL;DR

This paper introduces a distributed vision-based state estimation method for cooperative rotorcraft payload transport in GPS-denied environments, enhancing robustness and scalability over prior centralized approaches.

Contribution

It presents a novel decentralized estimation framework using onboard cameras and a Distributed Extended Information Filter for resilient multilift payload tracking.

Findings

Effective payload state estimation in GPS-denied environments.

Robustness to sensor and communication dropouts demonstrated.

Scalable approach suitable for real-world UAV multilift operations.

Abstract

Transporting heavy or oversized slung loads using rotorcraft has traditionally relied on single-aircraft systems, which limits both payload capacity and control authority. Cooperative multilift using teams of rotorcraft offers a scalable and efficient alternative, especially for infrequent but challenging "long-tail" payloads without the need of building larger and larger rotorcraft. Most prior multilift research assumes GPS availability, uses centralized estimation architectures, or relies on controlled laboratory motion-capture setups. As a result, these methods lack robustness to sensor loss and are not viable in GPS-denied or operationally constrained environments. This paper addresses this limitation by presenting a distributed and decentralized payload state estimation framework for vision-based multilift operations. Using onboard monocular cameras, each UAV detects a fiducial marker on the payload and estimates its relative pose. These measurements are fused via a Distributed and Decentralized Extended Information Filter (DDEIF), enabling robust and scalable estimation that is resilient to individual sensor dropouts. This payload state estimate is then used for closed-loop trajectory tracking control. Monte Carlo simulation results in Gazebo show the effectiveness of the proposed approach, including the effect of communication loss during flight.