Practical and accurate calibration of RGB-D cameras using spheres

Staranowicz, Aaron; Brown, Garrett R.; Morbidi, Fabio; Mariottini, Gian-Luca

doi:10.1016/j.cviu.2015.03.013

Cited by 43 publications

(30 citation statements)

References 34 publications

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“…Moreover, to give a comparison metric, also the factory-provided transformation is reported. The values obtained are similar to those obtainable with other state-of-the-art calibration tools for RGB-D devices [25], [40], [26], [22].…”

Section: Testing the Whole Proceduressupporting

confidence: 83%

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Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

2018

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Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumergrade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications (e.g., highly accurate 3D environment reconstruction and mapping, high precision object recognition and localization, . . . ). In this paper, we propose a human-friendly, reliable and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple. Our approach is based on a novel two components error model. This model unifies the error sources of RGB-D pairs based on different technologies, such as structured-light 3D cameras and time-of-flight cameras. Our method provides some important advantages compared to other state-of-the-art systems: it is general (i.e., well suited for different types of sensors), based on an easy and stable calibration protocol, provides a greater calibration accuracy, and has been implemented within the ROS robotics framework. We report detailed experimental validations and performance comparisons to support our statements.

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Section: Testing the Whole Proceduressupporting

confidence: 83%

“…Moreover, they used the 4 corners of the checkerboard plane as the initial guess of the relative displacement between the cameras and the depth sensor. For short distances their approach seems to obtain good results, as reported also in [26], [27].…”

Section: Related Worksupporting

confidence: 64%

Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

2018

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“…Pattern-based calibration methods are usually developed for specific pairs of sensors. For example: a camera and a 2D laser scanner [5], or monocular and depth cameras [6]. Using specific calibration techniques to cover all sensors pairs in a multi-sensor configuration makes the calibration process complex and almost impractical.…”

Section: Planar Motionmentioning

confidence: 99%

“…For example, the work in [5] uses scene corners (orthogonal trihedrons) to calibrate a 2D laser scanner and a camera. The information provided by a spherical object is used in [6] to calibrate a camera and a depth sensor. The overlap requirement between the sensors' field of view is relaxed in [7] to calibrate multiple depth cameras from the common observation of planar structures.…”

Section: Related Workmentioning

confidence: 99%

Automatic Multi-Sensor Extrinsic Calibration For Mobile Robots

Zuñiga-Noël

Ruiz-Sarmiento

Gomez-Ojeda

et al. 2019

IEEE Robot. Autom. Lett.

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In order to fuse measurements from multiple sensors mounted on a mobile robot, it is needed to express them in a common reference system through their relative spatial transformations. In this paper, we present a method to estimate the full 6DoF extrinsic calibration parameters of multiple heterogeneous sensors (Lidars, Depth and RGB cameras) suitable for automatic execution on a mobile robot. Our method computes the 2D calibration parameters (x, y, yaw) through a motion-based approach, while for the remaining 3 parameters (z, pitch, roll) it requires the observation of the ground plane for a short period of time. What set this proposal apart from others is that: i) all calibration parameters are initialized in closed form, and ii) the scale ambiguity inherent to motion estimation from a monocular camera is explicitly handled, enabling the combination of these sensors and metric ones (Lidars, stereo rigs, etc.) within the same optimization framework. We provide a formal definition of the problem, as well as of the contributed method, for which a C++ implementation has been made publicly available. The suitability of the method has been assessed in simulation an with real data from indoor and outdoor scenarios. Finally, improvements over state-of-the-art motion-based calibration proposals are shown through experimental evaluation.

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“…Staranowicz et al [SBMM15] have used a video of a spherical object moving in front the camera as input to their method. After a robust feature-extraction process, their algorithm infers an initial estimation of the depth, as well as the other calibration parameters, and then it performs a refinement estimate of the different parameters.…”

Section: Depth Cameras Calibrationmentioning

confidence: 99%

Calibrating Low-cost Structured-light 3D Sensors

Chakib¹,

Mérillou²,

Vincent³

et al. 2018

WSCG 2018 - Full Papers Proceedings

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Consumer-grade RGB-D cameras are widely accessible, but they suffer from a lack of accuracy when compared to professional-grade 3D scanning solutions. In this paper, we propose a new method for calibrating an Intel RealSense SR300 camera, adaptable to other structured light sensors. The method uses classical checkerboard calibration and a coordinate-measuring machine (CMM) based setup with a calibrating plane. It delivers better results than the manufacturers settings.

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Practical and accurate calibration of RGB-D cameras using spheres

Cited by 43 publications

References 34 publications

Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

Automatic Multi-Sensor Extrinsic Calibration For Mobile Robots

Calibrating Low-cost Structured-light 3D Sensors

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