A Review of RGB-D Camera Calibration Methods

doi:10.11947/j.JGGS.2021.0402

Abstract

Abstract:

RGB-D camera is a new type of sensor, which can obtain the depth and texture information in an unknown 3D scene simultaneously, and they have been applied in various fields widely. In fact, when implementing such kinds of applications using RGB-D camera, it is necessary to calibrate it first. To the best of our knowledge, at present, there is no existing a systemic summary related to RGB-D camera calibration methods. Therefore, a systemic review of RGB-D camera calibration is concluded as follows. Firstly, the mechanism of obtained measurement and the related principle of RGB-D camera calibration methods are presented. Subsequently, as some specific applications need to fuse depth and color information, the calibration methods of relative pose between depth camera and RGB camera are introduced in Section 2. Then the depth correction models within RGB-D cameras are summarized and compared respectively in Section 3. Thirdly, considering that the angle of the view field of RGB-D camera is smaller and limited to some specific applications, we discuss the calibration models of relative pose among multiple RGB-D cameras in Section 4. At last, the direction and trend of RGB-D camera calibration are prospected and concluded.

Key words: RGB-D camera calibration; relative pose; depth correction; multiple RGB-D cameras

Chenyang ZHANG,Teng HUANG,Yueqian SHEN. A Review of RGB-D Camera Calibration Methods[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(4): 11-33.

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Keyword	IEEE Xplore	Science Direct	Springer Link
“RGB-D camera calibration”	13	8	19
“RGB-D sensor calibration”	5	8	18
“RGB-D camera”	273	164	58
“RGB-D sensor”	132	165	35
“ToF camera”	544	100	4864
“Kinect”	1279	231	488

Sensor	Field of angle(H×V)	Size of Color image/pixel	Frame rate of RGB image/fps	Size of Depth image/pixel	Range of Depth/m	Frame rate of Depth image/fps	Technology
Kinect-1	57.5°×43.5°	640×480	30	640×480	0.8~4.0	30	SL
Kinect-2	70°×60°	1920×1080	30	512×424	0.5~4.2	30	ToF
XtionProLive	58°×45°	1280×1024	30	640×480	0.8~3.5	30	SL
Real Sense	59°×46°	1920×1080	30/60	640×480	0.8~1.2	30/60	SL
Structure Sensor	59°×45°	640×480	30	640×480	0.4~3.5	30	SL
Camrine1.08	57.5°×45°	640×480	30	640×480	0.8~3.5	30	SL
Camrine1.09	57.5°×45°	640×480	30	640×480	0.35~1.4	30	SL
Azure Kinect	75°×65°	Multiple	30	Multiple	0.5~5.46	30	ToF

Model	Year	Input	Calibration target	Open source	Applicable camera
Chen	2019	C,D	Checkerboard+MoCap	N	SL
Yamazoe	2018	I	Checkerboard	N	SL
Walid D	2017	I,C,D,Z	Checkerboard	N	SL
Quenzel	2017	C,Z	SLAM	N	SL/TOF
CiccoM	2015	Z	No	N	SL/TOF
Ferstl	2015	Z	Custom	N	SL/TOF
Gui	2014	Z	3D Calibration Field	N	SL/TOF
Jin	2014	Z,C,D	Cubic	N	SL
Canessa	2014	Z,I,C	Checkerboard	N	SL
Guo	2014	I,C	Checkerboard	N	SL
Teichman	2013	C,Z	SLAM	N	SL/TOF
Raposo	2013	Z,C	Checkerboard	Y	SL
Nguyen	2012	Z	No	N	SL/TOL
Kho	2012	C,D,Z,I	Checkerboard	N	SL
Daniel	2012	D C	Checkerboard	Y	SL
Daniel	2011	Z,C	Checkerboard	N	SL
Zhang	2011	Z C	Checkerboard	Y	SL
Smisek	2011	I,C,Z	Checkerboard	Y	SL

Model	Year	Input	Reference depth acquisition	Open source
Che	2018	Z	3D calibration field	N
Lachat E	2015	Z	Checkerboard	N
JiyoungJ	2015	Z	Self Calibration	N
Chow	2013	Z,C	Self Calibration	N
Belhedi	2012	Z	Self Calibration	N
Shim H	2012	C,Z,I	CCD Camera	N
Linder	2010	I,C	Self Calibration	N
Schiller	2008	Z	Self Calibration	N
YoungM	2008	—	Self Calibration	N
Fuchs	2008	Z	Robot Arm	N
Linder	2007	Z C	CCD Camera	N
Linder	2006	Z	—	N