Maximum likelihood mapping with spectral image registration
Maximum likelihood mapping with spectral image registration
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光谱图像配准的最大似然映射
DOI：
10.1109/robot.2010.5509366
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
K. Pathak
中科院分区：
文献类型：
--
作者：
M. Pfingsthorn;A. Birk;Sören Schwertfeger;H. Bülow;K. Pathak
概率映射的核心挑战是从数据配准方法中提取有意义的不确定性信息。虽然已经在基于 ICP 的扫描匹配方法中对此进行了研究，但尚未分析其他配准方法。本文介绍了基于傅里叶梅林的图像配准算法的不确定性分析，据我们所知，这是第一个涉及光谱配准的算法。从仅相位匹配滤波器的结果中提取协方差矩阵，将其解释为概率质量函数。该方法嵌入到同步定位与建图 (SLAM) 的位姿图实现中，并通过水下领域的实验进行验证。
A core challenge in probabilistic mapping is to extract meaningful uncertainty information from data registration methods. While this has been investigated in ICP-based scan matching methods, other registration methods have not been analyzed. In this paper, an uncertainty analysis of a Fourier Mellin based image registration algorithm is introduced, which to our knowledge is the first of its kind involving spectral registration. A covariance matrix is extracted from the result of a Phase-Only Matched Filter, which is interpreted as a probability mass function. The method is embedded in a pose graph implementation for Simultaneous Localization and Mapping (SLAM) and validated with experiments in the underwater domain.