Satellite Image-based UAV Localization using Siamese Neural Network

被引：0

作者：

Ahn, Seong-Ha ^{[1
]}

Kang, Ho-Sun ^{[1
]}

Lee, Jang-Myung ^{[1
]}

机构：

[1] Pusan Natl Univ, Dept Elect Engn, 2,Busandaehak Ro 63beon Gil, Busan 46241, South Korea

来源：

PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS (ICAROB 2021) | 2021年

基金：

新加坡国家研究基金会;

关键词：

visual localization; uav; satellite image; siamese neural network; image retrieval;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present a method for UAV localization using pre-existing satellite images. The use of Unmanned Aerial Vehicles (UAVs) has rapidly increased in several applications such as surveillance, search, and defense. When in GPS-denied situations, however, the onboard GPS signal may be noisy or inaccurate. The proposed method is based on a Siamese Neural Network that contains two instances of the same neural architecture and weights. Siamese Neural Network learns the similarity metric so that can recognize the same place from two raw images. Convolutional Neural Network is used as a backbone in Siamese Neural Network to overcome variation due to differences such as perspective, shadow angle, and presence of vehicles. We describe UAV localization pipeline and a dataset for training and testing our networks. Finally, the performance of the proposed method was shown in accuracy.

引用

页码：513 / 516

页数：4