CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination

• URL: http://arxiv.org/abs/2209.05824v2
• Date: Wed, 06 Nov 2024 07:23:52 GMT
• Title: CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination
• Authors: Guangyang Zeng, Shiyu Chen, Biqiang Mu, Guodong Shi, Junfeng Wu,
• Abstract summary: We propose a consistent solver, named emphC, with bias elimination. We analyze subtract bias based on which a closed-form least-squares solution is obtained. 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.
• Score: 10.099598169569383
• License:
• Abstract: 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.

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