Fast Registration Based on Noisy Planes With Unknown Correspondences for 3-D Mapping
Fast Registration Based on Noisy Planes With Unknown Correspondences for 3-D Mapping
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DOI：
10.1109/tro.2010.2042989
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发表时间：
2010-06-01
影响因子：
7.8
通讯作者：
Poppinga, Jann
中科院分区：
文献类型：
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作者：
Pathak, Kaustubh;Birk, Andreas;Poppinga, Jann
我们提出了一种机器人姿态注册算法，该算法完全基于从三维（3-D）传感器采样的点云中提取的大型平面面片。这种方法提供了传统点对点迭代最近点 (ICP) 算法、其点对平面变体以及更新的基于网格的算法（例如 3D 正态分布变换 (NDT)）的替代方案。首先通过考虑平面提取期间计算的平面参数不确定性推导最小二乘位姿估计的表达式来解决已知平面对应的简单情况。还导出了协方差的封闭式表达式。为了完善解决方案，我们提出了一种称为最小不确定最大一致性（MUMC）的新算法，通过最小化配置空间中的不确定性体积来最大化几何一致性来确定未知平面对应。给出了三个 3D 传感器（即 Swiss-Ranger、南佛罗里达大学 Odetics 激光探测和测距以及驱动的 SICK S300）的实验结果。前两个具有低视场 (FOV) 和中等范围，而第三个具有更大的 FOV 和范围。实验结果表明，在平面丰富的环境中，这种方法不仅比基于点或基于网格的方法更稳健，而且速度更快，需要的内存显着减少，并且提供了基于平面补丁的更清晰的可视化。
We present a robot-pose-registration algorithm, which is entirely based on large planar-surface patches extracted from point clouds sampled from a three-dimensional (3-D) sensor. This approach offers an alternative to the traditional point-to-point iterative-closest-point (ICP) algorithm, its point-to-plane variant, as well as newer grid-based algorithms, such as the 3-D normal distribution transform (NDT). The simpler case of known plane correspondences is tackled first by deriving expressions for least-squares pose estimation considering plane-parameter uncertainty computed during plane extraction. Closed-form expressions for co-variances are also derived. To round-off the solution, we present a new algorithm, which is called minimally uncertain maximal consensus (MUMC), to determine the unknown plane correspondences by maximizing geometric consistency by minimizing the uncertainty volume in configuration space. Experimental results from three 3-D sensors, viz., Swiss-Ranger, University of South Florida Odetics Laser Detection and Ranging, and an actuated SICK S300, are given. The first two have low fields of view (FOV) and moderate ranges, while the third has a much bigger FOV and range. Experimental results show that this approach is not only more robust than point-or grid-based approaches in plane-rich environments, but it is also faster, requires significantly less memory, and offers a less-cluttered planar-patches-based visualization.