Vision-Based Autonomous Landing for the MPC Controlled Fixed Wing UAV

This work introduces a novel vision-based autonomous landing system for fixed-wing UAVs optimized for GPS-denied environments. We combine vSLAM with the linear MPC strategy. A key innovation is to use an SVD-based Kalman filter in vSLAM, which significantly improves map point update accuracy and efficiency by reducing noise. The system precisely defines the landing area using image segmentation and Watershed Transform for real-time vSLAM data, then draws a rotated bounding box. This visual data feeds the linearized MPC, which computes the optimal control inputs which are longitudinal acceleration, yaw rate, vertical velocity to guide the UAV along the landing trajectory. Simulation results confirm the robust and effective performance of our integrated vSLAM-MPC architecture in precisely guiding the UAV to the landing zone.