Vision-Based Distributed Formation Control of Unmanned Aerial Vehicles
This addresses formation control for UAVs in GPS-denied environments, though it appears incremental as it builds on existing vision-based methods with a new pose estimation algorithm.
The paper tackled the problem of enabling UAVs to autonomously achieve formations using only onboard visual feedback, eliminating the need for global positioning, and demonstrated successful formation in a simulated natural environment without markers.
We present a novel control strategy for a team of unmanned aerial vehicles (UAVs) to autonomously achieve a desired formation using only visual feedback provided by the UAV's onboard cameras. This effectively eliminates the need for global position measurements. The proposed pipeline is fully distributed and encompasses a collision avoidance scheme. In our approach, each UAV extracts feature points from captured images and communicates their pixel coordinates and descriptors among its neighbors. These feature points are used in our novel pose estimation algorithm, QuEst, to localize the neighboring UAVs. Compared to existing methods, QuEst has better estimation accuracy and is robust to feature point degeneracies. We demonstrate the proposed pipeline in a high-fidelity simulation environment and show that UAVs can achieve a desired formation in a natural environment without any fiducial markers.