兰萨克
全球定位系统
计算机科学
计算机视觉
人工智能
职位(财务)
匹配(统计)
算法
特征(语言学)
数学
图像(数学)
电信
语言学
统计
哲学
财务
经济
作者
Long Xin,Xin He,Xin Cui,Ziyu Liu
出处
期刊:Lecture notes in electrical engineering
日期:2023-01-01
卷期号:: 981-993
标识
DOI:10.1007/978-981-99-0479-2_89
摘要
Fast and accurate localization for unmanned aerial vehicle (UAV) navigation could avoid hazards when GPS is unavailable. A vision-based global positioning system is developed for UAV by means of satellite images from Google Map as reference. In this system, an ultra-robust and fast feature correspondence algorithm, called Grid-based motion statistics (GMS), is utilized for scene matching. GMS’s performance is comparable to the techniques many orders of magnitude slower, and it maintains high speed as the fast algorithms. A Least Median Square improved GMS algorithm (LMedS-GMS) is developed which is employed to solve the matching failure of the original GMS in challenging scenarios. Moreover, a robust filtering localization approach combining the random sample consensus algorithm (RANSAC) and the proposed LMedS-GMS is designed for locating the position of an UAV. Finally, a vision-based global positioning architecture is proposed using this method with the altitude and direction information from the airborne sensors. Experimental results based on offline data demonstrate that the proposed algorithm is superior to state-of-the-art methods in the accuracy and real-time performance.
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