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

Abstract

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.