Adopted image matching techniques for aiding indoor navigation

In indoor navigation and visual odometry, cameras may be used as an aiding algorithm for inertial navigation. Fast and accurate image matching is an important task used in various applications in computer vision and visual odometry. In this research, the performances of all recently available detect...

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Autores principales:	Mohamed Ramadan, Mohamed El Tokhey, Ayman Ragab, Tamer Fath-Allah, Ahmed Ragheb
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Visual odometry Feature detector Feature descriptor Image matching Indoor navigation RANSAC Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/50a4dca0f1634dd393bb4e1140751102
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spelling	oai:doaj.org-article:50a4dca0f1634dd393bb4e11407511022021-11-22T04:23:00ZAdopted image matching techniques for aiding indoor navigation2090-447910.1016/j.asej.2021.04.029https://doaj.org/article/50a4dca0f1634dd393bb4e11407511022021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2090447921002173https://doaj.org/toc/2090-4479In indoor navigation and visual odometry, cameras may be used as an aiding algorithm for inertial navigation. Fast and accurate image matching is an important task used in various applications in computer vision and visual odometry. In this research, the performances of all recently available detection, description, and matching techniques were compared. The detection techniques included HARRIS, GFTT, SIFT, SURF, STAR, FAST, ORB, MSER, Dense, and SimpleBlob. Feature description techniques included SIFT, SURF, ORB, and BRIEF. Finally, image matching techniques included BruteForce, BruteForce-L1, BruteForce-Hamming, BruteForce-HammingLUT, and FlannBased. The comparison was made between different kinds of geometric distortions and deformations, such as scaling, rotation, fish-eye distortion, and shearing. In this research, the lighting conditions and the shadowing effects were not taken into consideration.To perform this task, different types of transformations were manually applied to original images and computed with the matching evaluation parameters, such as the number of detected keypoints in images, the processing time, and the matching accuracies for each algorithm to show which algorithm was the best and most robust against these distortions. This work provided us with a perspective on and contributed to the field of image-based indoor navigation systems to recommend further research. The results showed that the ORB detector, the ORB descriptor, and either the BruteForce-Hamming or the BruteForce-HammingLUT matchers were favored to be used in indoor environments.Mohamed RamadanMohamed El TokheyAyman RagabTamer Fath-AllahAhmed RaghebElsevierarticleVisual odometryFeature detectorFeature descriptorImage matchingIndoor navigationRANSACEngineering (General). Civil engineering (General)TA1-2040ENAin Shams Engineering Journal, Vol 12, Iss 4, Pp 3649-3658 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Visual odometry Feature detector Feature descriptor Image matching Indoor navigation RANSAC Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Visual odometry Feature detector Feature descriptor Image matching Indoor navigation RANSAC Engineering (General). Civil engineering (General) TA1-2040 Mohamed Ramadan Mohamed El Tokhey Ayman Ragab Tamer Fath-Allah Ahmed Ragheb Adopted image matching techniques for aiding indoor navigation
description	In indoor navigation and visual odometry, cameras may be used as an aiding algorithm for inertial navigation. Fast and accurate image matching is an important task used in various applications in computer vision and visual odometry. In this research, the performances of all recently available detection, description, and matching techniques were compared. The detection techniques included HARRIS, GFTT, SIFT, SURF, STAR, FAST, ORB, MSER, Dense, and SimpleBlob. Feature description techniques included SIFT, SURF, ORB, and BRIEF. Finally, image matching techniques included BruteForce, BruteForce-L1, BruteForce-Hamming, BruteForce-HammingLUT, and FlannBased. The comparison was made between different kinds of geometric distortions and deformations, such as scaling, rotation, fish-eye distortion, and shearing. In this research, the lighting conditions and the shadowing effects were not taken into consideration.To perform this task, different types of transformations were manually applied to original images and computed with the matching evaluation parameters, such as the number of detected keypoints in images, the processing time, and the matching accuracies for each algorithm to show which algorithm was the best and most robust against these distortions. This work provided us with a perspective on and contributed to the field of image-based indoor navigation systems to recommend further research. The results showed that the ORB detector, the ORB descriptor, and either the BruteForce-Hamming or the BruteForce-HammingLUT matchers were favored to be used in indoor environments.
format	article
author	Mohamed Ramadan Mohamed El Tokhey Ayman Ragab Tamer Fath-Allah Ahmed Ragheb
author_facet	Mohamed Ramadan Mohamed El Tokhey Ayman Ragab Tamer Fath-Allah Ahmed Ragheb
author_sort	Mohamed Ramadan
title	Adopted image matching techniques for aiding indoor navigation
title_short	Adopted image matching techniques for aiding indoor navigation
title_full	Adopted image matching techniques for aiding indoor navigation
title_fullStr	Adopted image matching techniques for aiding indoor navigation
title_full_unstemmed	Adopted image matching techniques for aiding indoor navigation
title_sort	adopted image matching techniques for aiding indoor navigation
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/50a4dca0f1634dd393bb4e1140751102
work_keys_str_mv	AT mohamedramadan adoptedimagematchingtechniquesforaidingindoornavigation AT mohamedeltokhey adoptedimagematchingtechniquesforaidingindoornavigation AT aymanragab adoptedimagematchingtechniquesforaidingindoornavigation AT tamerfathallah adoptedimagematchingtechniquesforaidingindoornavigation AT ahmedragheb adoptedimagematchingtechniquesforaidingindoornavigation
_version_	1718418220480724992

Adopted image matching techniques for aiding indoor navigation

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