# Efficient Self-Supervised Neuro-Analytic Visual Servoing for Real-time Quadrotor Control

arXiv: https://arxiv.org/abs/2507.19878 · HTML: https://mlsanity.com/paper/2507.19878/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [INSTA-YOLO: Real-Time Instance Segmentation](https://mlsanity.com/paper/2102.06777/) (2021)
- [Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach](https://mlsanity.com/paper/2401.15785/) (2024)
- [Visual Servoing Approach for Autonomous UAV Landing on a Moving Vehicle](https://mlsanity.com/paper/2104.01272/) (2021)
- [C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision Transformer Networks](https://mlsanity.com/paper/2404.19276/) (2024)
- [Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Tracker](https://mlsanity.com/paper/2401.04377/) (2024)
- [Automated Cinematography with Unmanned Aerial Vehicles](https://mlsanity.com/paper/1712.04353/) (2017)
- [Improvement in the UAV position estimation with low-cost GPS, INS and vision-based system: Application to a quadrotor UAV](https://mlsanity.com/paper/1807.06117/) (2018)
- [High-Speed Interception Multicopter Control by Image-based Visual Servoing](https://mlsanity.com/paper/2404.08296/) (2024)
- [Imagine2Servo: Intelligent Visual Servoing with Diffusion-Driven Goal Generation for Robotic Tasks](https://mlsanity.com/paper/2410.12432/) (2024)
- [Deep Visual Servoing of an Aerial Robot Using Keypoint Feature Extraction](https://mlsanity.com/paper/2503.23171/) (2025)
- [An Image Based Visual Servo Approach with Deep Learning for Robotic Manipulation](https://mlsanity.com/paper/1909.07727/) (2019)
- [Integrating Vision Systems and STPA for Robust Landing and Take-Off in VTOL Aircraft](https://mlsanity.com/paper/2412.09505/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learning on the Fly: Replay-Based Continual Object Perception for Indoor Drones](https://mlsanity.com/paper/2602.13440/) (2026)

## Abstract (arXiv)

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. Through knowledge distillation, the student model achieves 11x faster inference compared to the teacher IBVS pipeline, while demonstrating similar control accuracy at a significantly lower computational and memory cost. Our vision-only self-supervised neuro-analytic control, enables quadrotor orientation and movement without requiring explicit geometric models or fiducial markers. The proposed methodology leverages simulation-to-reality transfer learning and is validated on a small drone platform in GPS-denied indoor environments. Our key contributions include: (1) an analytical IBVS teacher that solves numerical instabilities inherent in classical approaches, (2) 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, and (3) 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.