Pose aware estimation improves multi drone flight agility and safety
Towards Agile Vision-Based Multi-UAV Flight: Revisiting State Estimation
Robotics
Summary
Accurately tracking the position and movement of multiple flying drones nearby is important to avoid crashes and coordinate their flight. Past methods mainly used only the drones' positions from images, which causes delays when figuring out how fast and in which direction they are moving. The authors found that adding tilt angle measurements from visual detectors helps estimate the drones' movement more quickly and accurately. Their new method reduced errors in speed and acceleration estimates, enabling drones to react faster and follow each other more stably in flight.
What this means in practice
- •For drone developers: Improve onboard state estimation for multi-drone systems to achieve more agile flight and safer collision avoidance using pose-aware visual sensors.
- •For robotics control engineers: Enhance control algorithms for leader-follower UAV formations with faster and more accurate state information enabling stable maneuvers with high acceleration.
Authors
Michal Pliska, Matouš Vrba, Ondřej Víta, Martin Jiroušek, Viktor Walter, Martin Saska
Abstract
Agile multi-UAV flight requires accurate and low-latency onboard estimation of the kinematic states of neighboring UAVs for collision avoidance, motion coordination, etc. Most vision-based approaches rely on position-only measurements, inferring velocity and acceleration indirectly from displacement. We show that this introduces a fixed structural delay in the estimation of higher-order states, which limits the achievable agility. To address this, we propose to integrate tilt measurements, provided by a state-of-the-art visual detector, which inform about the thrust direction of co-planar multirotor UAVs. We benchmark four position-only and five pose-aware estimators, including a novel formulation of a linear thrust-constraining Kalman filter, on two real-world and one high-fidelity photorealistic simulated dataset over different levels of agility (3-21 m/s^2). In our setup, pose-aware estimation consistently reduces the average velocity and acceleration estimation errors by 40% and 57% across the three datasets with the proposed KF formulation outperforming the other estimators. Position-only filters exhibit a constant ~300 ms delay in acceleration step response independent of agility, whereas the tilt-constrained estimators operate near the physical response limit given by the camera frame-rate by observing the change in thrust direction before the displacement accumulates. In a closed-loop leader-follower simulated experiment with NMPC control, position-only estimation of the leader's state fails to facilitate stable hovering of the follower, while the proposed estimator enables tracking of lateral maneuvers exceeding 2g of acceleration.