Flexible and accurate camera calibration using grid spherical images

Liu, Zhen; Wu, Qun; Wu, Suining; Pan, Xiao

doi:10.1364/oe.25.015269

Cited by 62 publications

(15 citation statements)

References 24 publications

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“…In the descriptions of the experiments, the calibration algorithms based on the images of the circular points and orthogonal vanishing points are denoted as ICP and OVP, respectively. The methods using the bi-tangent lines of the projection conics [33], semidefinite programming [35], the common pole and polar with respect to two sphere images [20], grid spherical images [17], and homography deduced by conic features [40] are denoted as BTL, SDP, CPP, GSI, and HCF, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…When there are multiple images of the PAA conic, the intrinsic parameters of the camera can be uniquely determined. 5) Calibration based on 3D calibration objects [16], [17]:…”

Section: Introductionmentioning

confidence: 99%

“…3The linear approach runs faster than non-linear calibration algorithms while maintaining comparable accuracy [18], [19]. 4Without considering the algebraic relation between the sphere images and IAC [16], [17], [20], we initiated a new perspective to complete the camera calibration using the projective invariants of sphere images.…”

Section: Introductionmentioning

confidence: 99%

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Camera Calibration Using Projective Invariants of Sphere Images

2020

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Two novel linear camera calibration algorithms were investigated in this study. In general, an occluding contour circle of a sphere and the optical centre of a camera form a right circular cone, and the generalised eigenvectors of two right circular cones encode an infinity point that lies on two support planes containing the contour circle. In the image plane, a vanishing line can be determined by connecting two vanishing points. Furthermore, the direction of the diameter passing through the infinity point is orthogonal to the direction of its conjugate diameter; thus, a set of orthogonal vanishing points can be computed. The imaged circular points or orthogonal vanishing points provide constraints that enable the intrinsic parameters to be recovered completely. The results of simulations and comparison data obtained from actual experiments confirm the effectiveness and feasibility of the proposed algorithms. INDEX TERMS Camera calibration, conjugate diameters, generalized eigenvalue, right circular cone, vanishing point.

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Section: Methodsmentioning

confidence: 99%

“…When there are multiple images of the PAA conic, the intrinsic parameters of the camera can be uniquely determined. 5) Calibration based on 3D calibration objects [16], [17]:…”

Section: Introductionmentioning

confidence: 99%

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Camera Calibration Using Projective Invariants of Sphere Images

2020

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“…The values of f and b can be obtained by camera calibration, and d can be obtained by stereo vision matching. In summary, camera calibration and stereo vision matching are the key steps to achieve binocular vision, and the calibration accuracy and matching effect have a decisive influence on 3D reconstruction [28], [29].…”

Section: A the Principle Of Binocular Visionmentioning

confidence: 99%

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

Zhai

Zhang

et al. 2020

IEEE Access

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Rescue work after a coal mine accident is fraught with challenges and dangers. Considering the safety of rescue workers and the urgency of a rescue mission, it is necessary to use coal mine rescue robots to perform the tasks of environmental detection and rescue. As a key part of the robot sensing system, a visual sensor can provide much information about a scene. Among vision sensor types, binocular vision has the advantages of being noncontact and passive, and it is the key technology for a robot to acquire obstacle information and reconstruct a three-dimensional scene. Therefore, coal mine rescue robots based on binocular vision have become a popular research topic in the field of mine safety. First, the research status of camera calibration and stereo vision matching for binocular vision is systematically introduced in this paper. Second, the latest research progress on coal mine rescue robots based on binocular vision is reviewed from the perspective of technological applications and development. Finally, the technical challenges and future development trends of binocular vision in coal mine rescue robots are described. INDEX TERMS Coal mine rescue robots, binocular vision, camera calibration, stereo vision matching.

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“…A sphere has isotropic visibility from any view [12], [13]. Hence, calibration technology using only a sphere as the pattern has been studied widely [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

Two Separate Circles With Same-Radius: Projective Geometric Properties and Applicability in Camera Calibration

2020

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In the domain of computer vision, camera calibration is a key step in recovering the twodimensional Euclidean structure. Circles are considered important image features similar to points, lines, and conics. In this paper, a novel linear calibration method is proposed using two separate same-radius (SSR) circles as the calibration pattern. We show that the distinct pair of dual circles encodes three lines, two of which are parallel to each other and perpendicular to the remaining line. When any two coplanar or parallel circles degenerate to SSR circles, a solution can be found to recover another pair of parallel lines based on the geometric properties of the SSR circles. Using the vanishing points obtained as the key helper for determining the imaged circular points and the orthogonal vanishing points, we deduce the constraints on the image of the absolute conic (IAC) and then employ it for complete camera calibration. Furthermore, a closed-form solution for the extrinsic parameters can be obtained based on the projective invariance of the conic dual to the circular points. Evaluations based on simulated and real data confirmed the effectiveness and feasibility of the proposed algorithms. INDEX TERMS Camera calibration, conic dual, image of circular points, parallel lines, SSR circles.

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Flexible and accurate camera calibration using grid spherical images

Cited by 62 publications

References 24 publications

Camera Calibration Using Projective Invariants of Sphere Images

Camera Calibration Using Projective Invariants of Sphere Images

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

Two Separate Circles With Same-Radius: Projective Geometric Properties and Applicability in Camera Calibration

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