The perspective view of three points

Wolfe, William J.; Mathis, Donald W.; Sklair, Cheryl W.; Magee, Michael

doi:10.1109/34.67632

Cited by 103 publications

(61 citation statements)

References 12 publications

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“…The minimal number of correspondences to solve PnP problem is three. Some researches [11,12] had found that P3P problem with non-collinear points would result in as many as four solutions. Fischler [13] found that P4P problem with non -coplanar points had several solutions.…”

Section: Related Workmentioning

confidence: 99%

Vision Based Robust Pose Estimation System in Different Observed Situations for an Outdoor Quadrotor

Zheng¹,

Zhou²,

Wang³

2014

IJCA

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Section: Related Workmentioning

confidence: 99%

Vision Based Robust Pose Estimation System in Different Observed Situations for an Outdoor Quadrotor

Zheng¹,

Zhou²,

Wang³

2014

IJCA

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“…To measure the relative position and orientation between an object frame and a camera frame, monocular vision (Wu et al 2015) and stereo vision (Yang et al 2016) are usually employed as effective measurement tools. In general, monocular visual measurement uses the inherent geometric relationship among the target points (Wolfe et al 1991), while binocular stereo visual measurement uses triangulation to obtain the coordinates of the object in the camera frame (Zhai et al 2015;Knorr et al 2013). This paper will focus on visual measurement with monocular vision.…”

Section: Introductionmentioning

confidence: 99%

Global homography calibration for monocular vision-based pose measurement of mobile robots

Zhang

Wang

et al. 2017

Int J Intell Robot Appl

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Compared with the size of a calibration board, the inspected area within the camera field of view is much bigger in many applications such as visual surveillance systems. In this paper, we present a global image-to-ground homography calibration method to obtain the mapping between the image and the planar scene lying in the whole camera field of view. The calibration approach is proposed by fusing multiple local homography matrices, with each of them only reflecting the relationship between a small calibration board and its corresponding small image part. By further calibrating a height-related homography, visual measurement can be extended for arbitrary planar surfaces with known heights. The proposed method presents such merits as no requirement for camera internal parameters, high calibration accuracy, and ease of implementation. Experimental results show that (1) the proposed global homography method achieves more accurate measurement results than the local homography method; (2) the heightrelated homography is calibrated with high accuracy; (3) the proposed approach can be used for mobile robot localization with accuracy close to the performance limit of a monocular camera.

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“…Camera pose can also be computed from corner structure (Shi et al, 2004). Besides these particular objects, many researches investigate the localization problem from a set of abstract points or lines, namely known as the perspective-n-point (PnP) and perspective-n-line (PnL) problems (Wolfe et al, 1991;Kneip et al, 2011;Hu & Matsuyama, 2011). Besides points and lines, the position can also be computed from a plane or planar object.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Perspective-Plane (Bpp) for Localization

Hu¹,

Matsuyama

2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:The "perspective-plane" problem proposed in this paper is similar to the "perspective-n-point (PnP)" or "perspective-n-line (PnL)" problems, yet with broader applications and potentials, since planar scenes are more widely available than control points or lines in practice. We address this problem in the Bayesian framework and propose the "Bayesian perspective-plane (BPP)" algorithm, which can deal with more generalized constraints rather than type-specific ones to determine the plane for localization. Computation of the plane normal is formulated as a maximum likelihood problem, and is solved by using the Maximum Likelihood Searching Model (MLS-M). Two searching modes of 2D and 1D are presented. With the computed normal, the plane distance and the position of the object or camera can be computed readily. The BPP algorithm has been tested with real image data by using different types of scene constraints. The 2D and 1D searching modes were illustrated for plane normal computation. The results demonstrate that the algorithm is accurate and generalized for object localization.

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The perspective view of three points

Cited by 103 publications

References 12 publications

Vision Based Robust Pose Estimation System in Different Observed Situations for an Outdoor Quadrotor

Vision Based Robust Pose Estimation System in Different Observed Situations for an Outdoor Quadrotor

Global homography calibration for monocular vision-based pose measurement of mobile robots

Bayesian Perspective-Plane (Bpp) for Localization

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