CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866909378412544000 |
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| author | Zeng, Guangyang Chen, Shiyu Mu, Biqiang Shi, Guodong Wu, Junfeng |
| author_facet | Zeng, Guangyang Chen, Shiyu Mu, Biqiang Shi, Guodong Wu, Junfeng |
| contents | The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies. With the development of feature extraction techniques, a large number of feature points might be available in a single shot. It is promising to devise a consistent estimator, i.e., the estimate can converge to the true camera pose as the number of points increases. To this end, we propose a consistent PnP solver, named \emph{CPnP}, with bias elimination. Specifically, linear equations are constructed from the original projection model via measurement model modification and variable elimination, based on which a closed-form least-squares solution is obtained. We then analyze and subtract the asymptotic bias of this solution, resulting in a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed to refine the consistent solution. Our proposed estimator is efficient in terms of computations -- it has $O(n)$ computational complexity. Experimental tests on both synthetic data and real images show that our proposed estimator is superior to some well-known ones for images with dense visual features, in terms of estimation precision and computing time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_05824 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination Zeng, Guangyang Chen, Shiyu Mu, Biqiang Shi, Guodong Wu, Junfeng Computer Vision and Pattern Recognition Robotics The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies. With the development of feature extraction techniques, a large number of feature points might be available in a single shot. It is promising to devise a consistent estimator, i.e., the estimate can converge to the true camera pose as the number of points increases. To this end, we propose a consistent PnP solver, named \emph{CPnP}, with bias elimination. Specifically, linear equations are constructed from the original projection model via measurement model modification and variable elimination, based on which a closed-form least-squares solution is obtained. We then analyze and subtract the asymptotic bias of this solution, resulting in a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed to refine the consistent solution. Our proposed estimator is efficient in terms of computations -- it has $O(n)$ computational complexity. Experimental tests on both synthetic data and real images show that our proposed estimator is superior to some well-known ones for images with dense visual features, in terms of estimation precision and computing time. |
| title | CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2209.05824 |