论文标题
CPNP：对于消除偏置的透视n点问题的一致姿势估计器
CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination
论文作者
论文摘要
在计算机视觉和摄影测量社会中，Perspective-N-point（PNP）问题已被广泛研究。随着特征提取技术的发展，单镜头可能会提供大量功能点。有望设计一个一致的估计器，即，随着点数的增加，估计值可以收敛到真实的相机姿势。为此，我们提出了一个一致的PNP求解器，称为\ emph {cpnp}，并消除了偏差。具体而言，线性方程是通过测量模型修改和可变消除的原始投影模型构建的，基于该模型，可以获得封闭形式最小二乘解决方案。然后，我们分析并减去该溶液的渐近偏置，从而产生一致的估计值。此外，执行高斯 - 纽顿（GN）迭代以完善一致的解决方案。我们提出的估计器在计算方面有效 - 它具有$ o（n）$计算复杂性。关于合成数据和真实图像的实验测试表明，就估计精度和计算时间而言，我们提出的估计量优于一些具有密集视觉特征的图像的知名图像。
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.
