An image matching system for autonomous UAV navigation based on neural network

J. Braga, H. Velho, G. Conte, P. Doherty, E. H. Shiguemori
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引用次数: 35

Abstract

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.
基于神经网络的无人机自主导航图像匹配系统
本文提出了一种基于航拍图像、飞行时间图像和航空地理参考图像的图像匹配系统,用于全球导航卫星系统(GNSS)故障情况下的无人机位置估计。该图像匹配系统基于航拍图像和地理参考图像的边缘检测以及这些边缘图像的后验自动配准(无人机位置估计)。边缘检测过程由具有最优结构的人工神经网络(ANN)完成。并与Sobel和Canny边缘提取滤波器进行了比较。通过互相关处理实现图像的自动配准。采用多粒子碰撞算法(MPCA)确定神经网络的最优结构。图像匹配系统在低成本/低消耗的便携式计算机上实现。在实际飞行试验数据上对图像匹配系统进行了测试,取得了令人鼓舞的结果。将介绍使用真实飞行试验数据的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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