Embedded Low Power Controller for Autonomous Landing of UAV Using Artificial Neural Network

We present real-time, stereo vision based autonomous landing system for small Unmanned Aerial Vehicles (UAV) onto an unknown landing target. The paper describes the algorithms and design of FPGA based co-processor implementing Artificial Neural Network (ANN) to implement real time object tracking, 3D position estimation using Visual Odometry(VO), Horizontal displacement and Euclidean distance from landing target. This approach doesn´t require any explicit marker or landing target, it estimates attitude, track safe landing area, and compute distance and horizontal displacement form landing target. Experimental results show suitability of the real-time stereo vision landing approach using FPGA for tracking, that doesn´t require any explicit landing marker.