arXiv 1607.081122016-07-27cs.CV

MLPnP --透视n点问题的实时的最大似然解

MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem

Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院摄影测量与遥感研究所）

Steffen Urban, Jens Leitloff, Stefan Hinz

备注：: Submitted to the ISPRS congress (2016) in Prague. Oral Presentation. Published in ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 131-138
DOI：: 10.5194/isprs-annals-III-3-131-2016

摘要：

本文给出了透视n点问题的一个统计最优解。许多解决方案的Pests问题是几何最优的，但不考虑的不确定性的意见。另外，期望具有对所估计的相机姿态的旋转和平移参数的准确度的内部估计。因此，我们提出了一种新的最大似然解决方案的PSPILE问题，它结合了图像观测的不确定性，并保持实时的【形】能够在同一时间。此外，所提出的方法是通用的，因为它与3D方向矢量而不是2D图像点一起工作，因此能够处理任意的中心相机模型。这是通过将观测值的协方差矩阵投影（并因此减少）到相应的向量切空间来实现的。

英文摘要：

In this paper, a statistically optimal solution to the Perspective-n-Point (PnP) problem is presented. Many solutions to the PnP problem are geometrically optimal, but do not consider the uncertainties of the observations. In addition, it would be desirable to have an internal estimation of the accuracy of the estimated rotation and translation parameters of the camera pose. Thus, we propose a novel maximum likelihood solution to the PnP problem, that incorporates image observation uncertainties and remains real-time capable at the same time. Further, the presented method is general, as is works with 3D direction vectors instead of 2D image points and is thus able to cope with arbitrary central camera models. This is achieved by projecting (and thus reducing) the covariance matrices of the observations to the corresponding vector tangent space.

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