An image matching system for autonomous UAV navigation based on neural network
This paper proposes an image matching system using aerial images, captured in flight time, and aerial geo referenced images to estimate the Unmanned Aerial Vehicle (UAV) position in a situation of Global Navigation Satellite System (GNSS) failure. The image matching system is based on edge detection in the aerial and geo referenced image and posterior automatic image registration of these edge images (position estimation of UAV) The edge detection process is performed by an Artificial Neural Network (ANN) with an optimal architecture. A comparison with Sobel and Canny edge extraction filters is also provided. The automatic image registration is obtained by a cross correlation process. The ANN optimal architecture is set by the Multiple Particle Collision Algorithm (MPCA) The image matching system was implemented in a low cost/consumption portable computer. The image matching system has been tested on real flight test data and encouraging results have been obtained. Results using real flight test data will be presented.
2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)