Bayesian Perspective-plane (BPP) for Localization
Bayesian Perspective-plane (BPP) for Localization
复制标题
DOI：
10.5220/0003818902410246
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Zhaozheng Hu;T. Matsuyama
中科院分区：
文献类型：
--
作者：
Zhaozheng Hu;T. Matsuyama
本文提出的“透视平面”问题类似于“透视n点”或“透视n线”问题，但具有更广泛的应用和潜力，因为平面场景比控制点或控制线更广泛。我们在贝叶斯框架中解决这个问题，并提出了“贝叶斯透视平面（BPP）”算法，它可以处理更广义的约束，而不是类型特定的，以确定平面定位。平面法线的计算被公式化为最大似然问题，并使用最大似然搜索模型（MLS-M）来解决。提出了二维和一维两种搜索模式。利用计算的法线，可以容易地计算物体或相机的平面距离和位置。通过使用不同类型的场景约束，BPP算法已经用真实的图像数据进行了测试。给出了平面法向计算的二维和一维搜索模式。实验结果表明，该算法具有较高的定位精度和较强的通用性.
The "perspective-plane" problem proposed in this paper is similar to the "perspective-n-point (PnP)" or "perspective-n-line (PnL)" problems, yet with broader applications and potentials, since planar scenes are more widely available than control points or lines in practice. We address this problem in the Bayesian framework and propose the "Bayesian perspective-plane (BPP)" algorithm, which can deal with more generalized constraints rather than type-specific ones to determine the plane for localization. Computation of the plane normal is formulated as a maximum likelihood problem, and is solved by using the Maximum Likelihood Searching Model (MLS-M). Two searching modes of 2D and 1D are presented. With the computed normal, the plane distance and the position of the object or camera can be computed readily. The BPP algorithm has been tested with real image data by using different types of scene constraints. The 2D and 1D searching modes were illustrated for plane normal computation. The results demonstrate that the algorithm is accurate and generalized for object localization.