Adaptive quadcopter model predictive control using remote monocular vision

Abstract

This paper presents an adaptive control framework for quadcopter trajectory tracking in which all external state feedback is derived from a monocular video stream. Inspired by first-person view (FPV) piloting, the proposed system uses monocular video streamed to a remote ground station to estimate pose, identify dynamic parameters online, and compute control commands via model predictive control (MPC), while low-level attitude stabilisation is performed by the onboard flight controller in inertial measurement unit (IMU)-based angle mode. The architecture integrates ORB-SLAM3 for real-time pose estimation, an augmented-state Unscented Kalman Filter (UKF) for online estimation of internal quadcopter model parameters, and an MPC controller. All estimation and trajectory-level control computation is performed offboard, requiring only a hobby-grade quadcopter equipped with a monocular FPV camera, video transmitter, and radio-control receiver. Real-world experimental results demonstrate adaptation across quadcopter configurations and accurate tracking of multiple reference trajectories.