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

TL;DR

This paper introduces CPnP, a new consistent PnP solver with bias elimination that converges to the true camera pose as feature points increase, offering high accuracy and efficiency.

Contribution

The paper presents a novel bias-eliminating, consistent PnP estimator with a closed-form solution and linear complexity, improving accuracy and speed over existing methods.

Findings

Outperforms existing methods in estimation precision

Has linear computational complexity $O(n)$

Effective on both synthetic and real images

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.