Multi-UAV State EstimationExpert
Towards Agile Vision-Based Multi-UAV Flight: Revisiting State Estimation
This preprint introduces vision-based pose-aware state estimators for neighboring multirotor UAVs, integrating visual tilt measurements to infer thrust direction, including a new linear thrust-constrained Kalman filter. Across two real-world and one simulated dataset, pose-aware methods cut mean velocity and acceleration errors by 40% and 57% versus position-only filters, remove a ~300 ms acceleration delay, and enable simulated follower tracking of >2 g lateral maneuvers where position-only estimation fails. This matters for collision avoidance and coordination in agile multi-UAV operations.