A Real-Time Registration Algorithm of UAV Aerial Images Based on Feature Matching

This study aimed to achieve the accurate and real-time geographic positioning of UAV aerial image targets. We verified a method of registering UAV camera images on a map (with the geographic location) through feature matching. The UAV is usually in rapid motion and involves changes in the camera hea...

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Main Authors: Zhiwen Liu, Gen Xu, Jiangjian Xiao, Jingxiang Yang, Ziyang Wang, Siyuan Cheng
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Journal of Imaging
Subjects:
Online Access:https://www.mdpi.com/2313-433X/9/3/67
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author Zhiwen Liu
Gen Xu
Jiangjian Xiao
Jingxiang Yang
Ziyang Wang
Siyuan Cheng
author_facet Zhiwen Liu
Gen Xu
Jiangjian Xiao
Jingxiang Yang
Ziyang Wang
Siyuan Cheng
author_sort Zhiwen Liu
collection DOAJ
description This study aimed to achieve the accurate and real-time geographic positioning of UAV aerial image targets. We verified a method of registering UAV camera images on a map (with the geographic location) through feature matching. The UAV is usually in rapid motion and involves changes in the camera head, and the map is high-resolution and has sparse features. These reasons make it difficult for the current feature-matching algorithm to accurately register the two (camera image and map) in real time, meaning that there will be a large number of mismatches. To solve this problem, we used the SuperGlue algorithm, which has a better performance, to match the features. The layer and block strategy, combined with the prior data of the UAV, was introduced to improve the accuracy and speed of feature matching, and the matching information obtained between frames was introduced to solve the problem of uneven registration. Here, we propose the concept of updating map features with UAV image features to enhance the robustness and applicability of UAV aerial image and map registration. After numerous experiments, it was proved that the proposed method is feasible and can adapt to the changes in the camera head, environment, etc. The UAV aerial image is stably and accurately registered on the map, and the frame rate reaches 12 frames per second, which provides a basis for the geo-positioning of UAV aerial image targets.
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spelling doaj.art-538e7e3577ea4c3bac741e57047537152023-11-17T11:55:15ZengMDPI AGJournal of Imaging2313-433X2023-03-01936710.3390/jimaging9030067A Real-Time Registration Algorithm of UAV Aerial Images Based on Feature MatchingZhiwen Liu0Gen Xu1Jiangjian Xiao2Jingxiang Yang3Ziyang Wang4Siyuan Cheng5Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, ChinaNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, ChinaNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaThis study aimed to achieve the accurate and real-time geographic positioning of UAV aerial image targets. We verified a method of registering UAV camera images on a map (with the geographic location) through feature matching. The UAV is usually in rapid motion and involves changes in the camera head, and the map is high-resolution and has sparse features. These reasons make it difficult for the current feature-matching algorithm to accurately register the two (camera image and map) in real time, meaning that there will be a large number of mismatches. To solve this problem, we used the SuperGlue algorithm, which has a better performance, to match the features. The layer and block strategy, combined with the prior data of the UAV, was introduced to improve the accuracy and speed of feature matching, and the matching information obtained between frames was introduced to solve the problem of uneven registration. Here, we propose the concept of updating map features with UAV image features to enhance the robustness and applicability of UAV aerial image and map registration. After numerous experiments, it was proved that the proposed method is feasible and can adapt to the changes in the camera head, environment, etc. The UAV aerial image is stably and accurately registered on the map, and the frame rate reaches 12 frames per second, which provides a basis for the geo-positioning of UAV aerial image targets.https://www.mdpi.com/2313-433X/9/3/67SuperGluefeature matchingdronereal-time image registrationimage blockingtarget geolocation
spellingShingle Zhiwen Liu
Gen Xu
Jiangjian Xiao
Jingxiang Yang
Ziyang Wang
Siyuan Cheng
A Real-Time Registration Algorithm of UAV Aerial Images Based on Feature Matching
Journal of Imaging
SuperGlue
feature matching
drone
real-time image registration
image blocking
target geolocation
title A Real-Time Registration Algorithm of UAV Aerial Images Based on Feature Matching
title_full A Real-Time Registration Algorithm of UAV Aerial Images Based on Feature Matching
title_fullStr A Real-Time Registration Algorithm of UAV Aerial Images Based on Feature Matching
title_full_unstemmed A Real-Time Registration Algorithm of UAV Aerial Images Based on Feature Matching
title_short A Real-Time Registration Algorithm of UAV Aerial Images Based on Feature Matching
title_sort real time registration algorithm of uav aerial images based on feature matching
topic SuperGlue
feature matching
drone
real-time image registration
image blocking
target geolocation
url https://www.mdpi.com/2313-433X/9/3/67
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