Sparse Point Registration
Sparse Point Registration
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稀疏点配准
DOI：
10.1007/978-3-030-28619-4_52
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
H. Choset
中科院分区：
文献类型：
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作者：
Rangaprasad Arun Srivatsan;P. Vagdargi;H. Choset
这项工作引入了稀疏的点登记（SPR）方法，用于执行强大的注册，鉴于对象的几何模型和对象表面的稀疏点测量（<20）。基于手术的注册，操纵等。我们的SPR方法是迭代的，在每次迭代中，当前的最佳姿势估算都在产生的姿势中产生多种姿势，以估算廉价成本功能的最佳姿势。局部最佳注册。另一方面，PSPR除了使用各种标准数据集评估了局部的最佳选择，并且需要更少的参数。 - 在与其他方法进行比较时，发现DSPR和PSPR在初始姿势误差以及测量中的噪声方面也是如此。
This work introduces a Sparse Point Registration (SPR) method for performing robust registration given the geometric model of the object and few sparse point-measurements (<20) of the object’s surface. Such a method is of critical importance in applications such as probing-based surgical registration, manipulation, etc. Our approach for SPR is iterative and in each iteration, the current best pose estimate is perturbed to generate several poses. Among the generated poses, the best pose as evaluated by an inexpensive cost function is used to estimate the locally optimum registration. This process is repeated, until the pose converges within a tolerance bound. Two variants of the SPR are developed: deterministic (dSPR) and probabilistic (pSPR). Compared to the pSPR, the dSPR is faster in converging to the local optimum, and requires fewer parameters to be tuned. On the other hand, the pSPR provides uncertainty information in addition to the registration estimate. Both the approaches were evaluated using various standard data sets and the compared to results obtained using state-of-the-art methods. Upon comparison with other methods, both dSPR and pSPR were found to be robust to initial pose errors as well as noise in measurements. The effectiveness of the approach is also demonstrated with an application of robot-probing based registration.