Lightweight Learning-Based Feature Selection for Real-Time Optical Flow Navigation on a Quadrotor Platform

Abstract

Accurate state estimation in GPS-denied environments is critical for autonomous quadrotor navigation. Conventional visual-inertial odometry (VIO) pipelines rely on dense feature extraction and tracking, which increases computational cost and is prone to drift when low-quality features dominate. Although learning-based detectors improve robustness, most are too computationally heavy for embedded deployment. This paper proposes a lightweight learning-based feature selection framework that prunes unreliable features to enable efficient optical flow navigation. A compact Convolutional Neural Network (CNN) is employed, with its pruning threshold adaptively adjusted to maintain a stable number of reliable features. The CNN augments ORB and Lucas–Kanade optical flow in a multithreaded pipeline with rotational false-velocity compensation and EKF fusion. Experiments on the Quanser QDrone2 demonstrate up to 75–80% reduction in position RMSE and approximately 25–30% reduction in computation time compared to the Fourier-based Phase Correlation (FPC) method, while sustaining real-time performance above 120 Hz without reliance on external localization systems.