Robust Real-Time UAV Target Tracking with Onboard Vision-Based Yaw Controller
Rylan Malarchick
The rise of small, agile Unmanned Aerial Vehicles (UAVs) presents a significant challenge for autonomous detection and tracking systems, especially in cluttered, real-world environments. Standard machine learning approaches are prone to failure when faced with noisy, corrupted, and high-dimensional data streams, particularly those from onboard sensors on resource constrained hardware. This work presents AIRHOUND, a UAV platform designed to address these challenges through a robust, low latency p