A 3D Compensation Method for the Systematic Errors of Kinect V2

To reduce the 3D systematic error of the RGB-D camera and improve the measurement accuracy, this paper is the first to propose a new 3D compensation method for the systematic error of a Kinect V2 in a 3D calibration field. The processing of the method is as follows. First, the coordinate system betw...

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Autores principales:	Chang Li, Bingrui Li, Sisi Zhao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Kinect V2 RGB-D camera systematic error 3D compensation 3D calibration field Science Q
Acceso en línea:	https://doaj.org/article/ae4d865a71734792b34408d20328e0d6
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id	oai:doaj.org-article:ae4d865a71734792b34408d20328e0d6
record_format	dspace
spelling	oai:doaj.org-article:ae4d865a71734792b34408d20328e0d62021-11-25T18:54:32ZA 3D Compensation Method for the Systematic Errors of Kinect V210.3390/rs132245832072-4292https://doaj.org/article/ae4d865a71734792b34408d20328e0d62021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4583https://doaj.org/toc/2072-4292To reduce the 3D systematic error of the RGB-D camera and improve the measurement accuracy, this paper is the first to propose a new 3D compensation method for the systematic error of a Kinect V2 in a 3D calibration field. The processing of the method is as follows. First, the coordinate system between the RGB-D camera and 3D calibration field is transformed using 3D corresponding points. Second, the inliers are obtained using the Bayes SAmple Consensus (BaySAC) algorithm to eliminate gross errors (i.e., outliers). Third, the parameters of the 3D registration model are calculated by the iteration method with variable weights that can further control the error. Fourth, three systematic error compensation models are established and solved by the stepwise regression method. Finally, the optimal model is selected to calibrate the RGB-D camera. The experimental results show the following: (1) the BaySAC algorithm can effectively eliminate gross errors; (2) the iteration method with variable weights could better control slightly larger accidental errors; and (3) the 3D compensation method can compensate 91.19% and 61.58% of the systematic error of the RGB-D camera in the depth and 3D directions, respectively, in the 3D control field, which is superior to the 2D compensation method. The proposed method can control three types of errors (i.e., gross errors, accidental errors and systematic errors) and model errors and can effectively improve the accuracy of depth data.Chang LiBingrui LiSisi ZhaoMDPI AGarticleKinect V2RGB-D camerasystematic error3D compensation3D calibration fieldScienceQENRemote Sensing, Vol 13, Iss 4583, p 4583 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Kinect V2 RGB-D camera systematic error 3D compensation 3D calibration field Science Q
spellingShingle	Kinect V2 RGB-D camera systematic error 3D compensation 3D calibration field Science Q Chang Li Bingrui Li Sisi Zhao A 3D Compensation Method for the Systematic Errors of Kinect V2
description	To reduce the 3D systematic error of the RGB-D camera and improve the measurement accuracy, this paper is the first to propose a new 3D compensation method for the systematic error of a Kinect V2 in a 3D calibration field. The processing of the method is as follows. First, the coordinate system between the RGB-D camera and 3D calibration field is transformed using 3D corresponding points. Second, the inliers are obtained using the Bayes SAmple Consensus (BaySAC) algorithm to eliminate gross errors (i.e., outliers). Third, the parameters of the 3D registration model are calculated by the iteration method with variable weights that can further control the error. Fourth, three systematic error compensation models are established and solved by the stepwise regression method. Finally, the optimal model is selected to calibrate the RGB-D camera. The experimental results show the following: (1) the BaySAC algorithm can effectively eliminate gross errors; (2) the iteration method with variable weights could better control slightly larger accidental errors; and (3) the 3D compensation method can compensate 91.19% and 61.58% of the systematic error of the RGB-D camera in the depth and 3D directions, respectively, in the 3D control field, which is superior to the 2D compensation method. The proposed method can control three types of errors (i.e., gross errors, accidental errors and systematic errors) and model errors and can effectively improve the accuracy of depth data.
format	article
author	Chang Li Bingrui Li Sisi Zhao
author_facet	Chang Li Bingrui Li Sisi Zhao
author_sort	Chang Li
title	A 3D Compensation Method for the Systematic Errors of Kinect V2
title_short	A 3D Compensation Method for the Systematic Errors of Kinect V2
title_full	A 3D Compensation Method for the Systematic Errors of Kinect V2
title_fullStr	A 3D Compensation Method for the Systematic Errors of Kinect V2
title_full_unstemmed	A 3D Compensation Method for the Systematic Errors of Kinect V2
title_sort	3d compensation method for the systematic errors of kinect v2
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ae4d865a71734792b34408d20328e0d6
work_keys_str_mv	AT changli a3dcompensationmethodforthesystematicerrorsofkinectv2 AT bingruili a3dcompensationmethodforthesystematicerrorsofkinectv2 AT sisizhao a3dcompensationmethodforthesystematicerrorsofkinectv2 AT changli 3dcompensationmethodforthesystematicerrorsofkinectv2 AT bingruili 3dcompensationmethodforthesystematicerrorsofkinectv2 AT sisizhao 3dcompensationmethodforthesystematicerrorsofkinectv2
_version_	1718410611934625792

A 3D Compensation Method for the Systematic Errors of Kinect V2

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