Paper
14 August 2019 Pose estimation of a single circle based on 1D homography
Author Affiliations +
Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117943 (2019) https://doi.org/10.1117/12.2539871
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China
Abstract
Circular target has an important application in pose estimation based on vision by the virtue of anti-occlusion, anti-noise and easy recognition on the image. This paper focuses on the pose estimation problem when the projection of circle center is determined, and a pose calculation method is proposed based on 1D homography. Firstly, under the normalized image coordinates, a homography matrix is determined from three points, one is the projection of circle center, the other two are the intersections of the line through the projected center and the projected ellipse. According to the geometrical properties of the homography matrix, a linear method for computing the circular pose from multiple homography matrices is presented. Secondly, the reprojection errors of image points are taken as the objective function to optimize the linear solution results. Finally, the proposed method is compared with the two existing pose estimation methods through experiment. The experimental results show that the proposed method is slightly superior to the existing methods in terms of anti-noise performance and has obvious advantages in remote pose estimation.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yao-Wen Lv, Zichen Xu, Wei Liu, and Xi-Peng Xu "Pose estimation of a single circle based on 1D homography", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117943 (14 August 2019); https://doi.org/10.1117/12.2539871
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KEYWORDS
Cameras

Calibration

Imaging systems

Error analysis

Optimization (mathematics)

Communication engineering

Machine vision

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