A robust registration method using Huber ICP and low rank and sparse decomposition
A robust registration method using Huber ICP and low rank and sparse decomposition
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DOI：
10.1109/apsipa.2015.7415371
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
Qiaochu Zhao;X. Han;Yenwei Chen
中科院分区：
文献类型：
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作者：
Qiaochu Zhao;X. Han;Yenwei Chen
提出了一种基于低阶稀疏分解的多点云配准框架。首先利用Huber-ICP进行粗配准，将所有点云大致对齐到同一位置，然后进行稀疏和低秩分解提取所有点云的低秩子空间，以期达到离群点和无数据丢失的目的。最后，可以在每个点云之间进行精细的配准过程，不仅得到更准确的配准结果，而且还可以得到更精确的对应。通过人工数据验证了该方法对点云中孤立点的稳健性，并表明即使在点云中的某些点丢失的情况下，该方法仍能获得有效的结果。
This paper proposes a robust registration and alignment framework for multiple point clouds using low rank and sparse decomposition. A coarse registration phase utilizing Huber-ICP is firstly performed to roughly align all the point clouds to a same location, and then sparse and low rank decomposition is applied to extract the low rank subspace of all the point clouds, which is expected to be outlier and loss data free. Finally, a fine registration procedure can be carried out between each point clouds from this low rank space to not only a more accurate registration result but also a more precise correspondence. Robustness of our method for outliers contained in point clouds is verified through manufactured data and it also shows that an effective result can still be achieved even when some points in the cloud are lost.