Abstract
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.
Abstract (translated)
这项工作介绍了一种自监督神经分析、成本效益的模型，用于基于视觉的四旋翼飞行器控制。该模型中一个只有1.7M参数的小学生卷积网络（ConvNet）自动从改进后的基于图像的视觉伺服控制器（IBVS）这一分析教师那里学习。 我们的IBVS系统通过简化传统的视觉伺服方程来解决数值不稳定性，并实现了高效的稳定图像特征检测。通过知识蒸馏，学生的模型在推理速度上比老师的IBVS管道快11倍，同时表现出相似的控制精度，但在计算和内存成本方面显著降低。我们提出的仅依赖于视觉的自监督神经分析控制方法使四旋翼飞行器能够在不需要显式几何模型或标记点的情况下调整姿态与移动。 该方法利用了仿真到现实的迁移学习，并在一个小型无人机平台上通过GPS限制下的室内环境进行了验证。我们的主要贡献包括：(1)一种能够解决经典方法中固有数值不稳定的分析IBVS教师；(2)结合YOLOv11和基于U-Net的分割器的两阶段分割流水线，用于稳健地对前后车辆进行分割，从而准确估计目标的方向；(3)一个高效的双通道知识蒸馏系统，该系统能够将几何视觉伺服能力从分析IBVS教师转移到一个小而紧凑的学生神经网络上，该学生模型优于老师，并适用于实时机载部署。
URL
https://arxiv.org/abs/2507.19878
