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.

翻译：透视n点（PnP）问题在计算机视觉和摄影测量学界已被广泛研究。随着特征提取技术的发展，单次拍摄可能获得大量特征点。设计一种一致估计器（即估计值能随着点数增加收敛到真实相机姿态）前景广阔。为此，我们提出了一种具有偏差消除功能的一致PnP求解器，命名为 \emph{CPnP}。具体而言，通过测量模型修正和变量消元，从原始投影模型构建线性方程组，并基于此获得闭式最小二乘解。随后我们分析并减去该解的渐近偏差，从而得到一致估计。此外，执行高斯-牛顿（GN）迭代以优化一致解。我们提出的估计器在计算效率方面表现优异——其计算复杂度为 $O(n)$。在合成数据和真实图像上的实验测试表明，对于具有密集视觉特征的图像，我们提出的估计器在估计精度和计算时间方面均优于一些知名方法。