6DOF point cloud alignment using geometric algebra-based adaptive filtering
6DOF point cloud alignment using geometric algebra-based adaptive filtering
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DOI：
10.1109/wacv.2016.7477642
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Anas Al-Nuaimi;E. Steinbach;W. B. Lopes;C. G. Lopes
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文献类型：
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作者：
Anas Al-Nuaimi;E. Steinbach;W. B. Lopes;C. G. Lopes
在本文中，我们证明了一种基于几何代数的最小均方自适应滤波器（GA-LMS）可以用于恢复由一组点对应关联的两个点云的6自由度对齐。我们提出了一系列技术，使GA-LMS具有异常值（假对应）弹性，优于基于奇异值分解（SVD）的标准最小二乘（LS）方法。我们进一步展示了如何推导和计算GA-LMS的步长。
In this paper we show that a Geometric Algebra-based least-mean-squares adaptive filter (GA-LMS) can be used to recover the 6-degree-of-freedom alignment of two point clouds related by a set of point correspondences. We present a series of techniques that endow the GA-LMS with outlier (false correspondence) resilience to outperform standard least squares (LS) methods that are based on Singular Value Decomposition (SVD). We furthermore show how to derive and compute the step size of the GA-LMS.