A New Approach to Calibrate Range Image and Color Image from Kinect

Li, Shibo; Zhuo, Qiongfang

doi:10.1109/ihmsc.2012.156

Cited by 10 publications

(8 citation statements)

References 3 publications

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“…Geiger et al [17] proposed more complex calibration object, consisting of multiple checkerboards. Shibo and Qing [18] designed a specific calibration board with regularly-spaced drilled holes allowing their easy identification in both RGB images and depth maps.…”

Section: Related Workmentioning

confidence: 99%

Calibration of Kinect-type RGB-D sensors for robotic applications

Karan

2015

FME Transaction

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Section: Related Workmentioning

confidence: 99%

Calibration of Kinect-type RGB-D sensors for robotic applications

Karan

2015

FME Transaction

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“…Geiger et al [16] proposed more complex calibration object, consisting of multiple checkerboards. Shibo and Qing [17] designed a specific calibration board with regularly-spaced drilled holes allowing their easy identification in both RGB images and depth maps.…”

Section: Related Workmentioning

confidence: 99%

Accuracy improvements of consumer-grade 3D sensors for robotic applications

Karan¹

2013

2013 IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY)

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Recent advances in development of low-cost 3D sensors, such as Microsoft Kinect, bring attractive opportunities to robot system integrators. The accuracy provided by such sensors is generally unsatisfactory for many robotic applications, but it may be improved through calibration. This paper presents a calibration case study that is based on the sensor calibration procedure involving only a use of a simple checkerboard. It is shown that the calibration enables improving sensor accuracy 3 to 5 times, depending on the anticipated use of the sensor. Additionally, results obtained using different levels of complexity of calibration models reveal that depth measurement correction is an important component of calibration as it may reduce by 50% the errors in sensor reading.

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“…However, the corners of a checkerboard in depth images needed to be marked manually, and the factor of distorted pixels in the depth image was not included in their consideration [15,16]. Due to the difficulty in identifying the corners of a checkerboard in a depth image directly, a semi-transparent checkerboard [17] and a board with the same circular holes [18,19] were selected as the calibration objects to help identify the corners for calibrating an RGB-D camera. Based on the geometric property that any three points in a 1D object are collinear, a 1D object [20] such as a stick was used to calibrate the RGB-D camera, too.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the geometric property that any three points in a 1D object are collinear, a 1D object [20] such as a stick was used to calibrate the RGB-D camera, too. In the existing calibration models of [17,18,19,20], the disadvantage was that the depth values could affect the accuracy of extracting corners in the calibration objects, and the depth noise was the main factor limiting the accuracy of the calibration. Moreover, the calibration objects utilized in the models of [18,19] needed some certain production requirements: the smaller sizes of objects could not be detected accurately when depth images were captured at far distance, and it might lead to a smaller number of corners being obtained at close range.…”

Section: Introductionmentioning

confidence: 99%

“…In the existing calibration models of [17,18,19,20], the disadvantage was that the depth values could affect the accuracy of extracting corners in the calibration objects, and the depth noise was the main factor limiting the accuracy of the calibration. Moreover, the calibration objects utilized in the models of [18,19] needed some certain production requirements: the smaller sizes of objects could not be detected accurately when depth images were captured at far distance, and it might lead to a smaller number of corners being obtained at close range. Compared to the accuracy and practical operability of the calibration models presented in [15,16,17,18,19,20], the calibration model based on a spherical object [21] was proposed with a strong maneuverability and robustness to depth noise.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Model of RGB-D Camera Calibration Based on 3D Control Field

Zhang

Huang

Zhao

2019

Sensors

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With extensive application of RGB-D cameras in robotics, computer vision, and many other fields, accurate calibration becomes more and more critical to the sensors. However, most existing models for calibrating depth and the relative pose between a depth camera and an RGB camera are not universally applicable to many different kinds of RGB-D cameras. In this paper, by using the collinear equation and space resection of photogrammetry, we present a new model to correct the depth and calibrate the relative pose between depth and RGB cameras based on a 3D control field. We establish a rigorous relationship model between the two cameras; then, we optimize the relative parameters of two cameras by least-squares iteration. For depth correction, based on the extrinsic parameters related to object space, the reference depths are calculated by using a collinear equation. Then, we calibrate the depth measurements with consideration of the distortion of pixels in depth images. We apply Kinect-2 to verify the calibration parameters by registering depth and color images. We test the effect of depth correction based on 3D reconstruction. Compared to the registration results from a state-of-the-art calibration model, the registration results obtained with our calibration parameters improve dramatically. Likewise, the performances of 3D reconstruction demonstrate obvious improvements after depth correction.

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A New Approach to Calibrate Range Image and Color Image from Kinect

Cited by 10 publications

References 3 publications

Calibration of Kinect-type RGB-D sensors for robotic applications

Calibration of Kinect-type RGB-D sensors for robotic applications

Accuracy improvements of consumer-grade 3D sensors for robotic applications

A New Model of RGB-D Camera Calibration Based on 3D Control Field

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