This work was accepted at the International Conference on Computer Vision (ICCV) 2025 Honolulu, Hawaii, USA.

This work introduces a self-supervised neuro-analytical, cost efficient, model for visual-based quadrotor control in which a small 1.7M parameters student ConvNet learns automatically from an analytical teacher, an improved image-based visual servoing (IBVS) controller. Our IBVS system solves numerical instabilities by reducing the classical visual servoing equations and enabling efficient stable image feature detection.

Our key contributions include: 

• An analytical IBVS teacher that solves numerical instabilities inherent in classical approaches

• A two-stage segmentation pipeline combining YOLOv11 with a U-Net-based mask splitter for robust anterior-posterior vehicle segmentation to correctly estimate the orientation of the target 

• An efficient knowledge distillation dual-path system, which transfers geometric visual servoing capabilities from the analytical IBVS teacher to a compact and small student neural network that outperforms the teacher, while being suitable for real-time onboard deployment.

Real-world analytical teacher: evolution of drone control commands and tracking errors across flight trajectory.

Real-world small ConvNet student, which distills the analytical teacher: evolution of drone control commands and tracking errors across flight trajectory.