Choi Ouk, Hwang Wonjun
Department of Electronics Engineering, Incheon National University, Yeonsu-gu, Incheon 22012, Korea.
Department of Software and Computer Engineering and Department of Artificial Intelligence, Ajou University, Yeongtong-gu, Suwon 16499, Korea.
Sensors (Basel). 2021 Oct 23;21(21):7023. doi: 10.3390/s21217023.
In the last stage of colored point cloud registration, depth measurement errors hinder the achievement of accurate and visually plausible alignments. Recently, an algorithm has been proposed to extend the Iterative Closest Point (ICP) algorithm to refine the measured depth values instead of the pose between point clouds. However, the algorithm suffers from numerical instability, so a postprocessing step is needed to restrict erroneous output depth values. In this paper, we present a new algorithm with improved numerical stability. Unlike the previous algorithm heavily relying on point-to-plane distances, our algorithm constructs a cost function based on an adaptive combination of two different projected distances to prevent numerical instability. We address the problem of registering a source point cloud to the union of the source and reference point clouds. This extension allows all source points to be processed in a unified filtering framework, irrespective of the existence of their corresponding points in the reference point cloud. The extension also improves the numerical stability of using the point-to-plane distances. The experiments show that the proposed algorithm improves the registration accuracy and provides high-quality alignments of colored point clouds.
在彩色点云配准的最后阶段，深度测量误差阻碍了精确且视觉上合理的对齐的实现。最近，有人提出了一种算法，将迭代最近点（ICP）算法进行扩展，以细化测量的深度值而非点云之间的位姿。然而，该算法存在数值不稳定性，因此需要一个后处理步骤来限制错误的输出深度值。在本文中，我们提出了一种具有改进数值稳定性的新算法。与之前严重依赖点到平面距离的算法不同，我们的算法基于两种不同投影距离的自适应组合构建一个代价函数，以防止数值不稳定。我们解决了将源点云配准到源点云和参考点云的并集的问题。这种扩展允许在统一的滤波框架中处理所有源点，而不管它们在参考点云中对应点的存在情况。该扩展还提高了使用点到平面距离时的数值稳定性。实验表明，所提出的算法提高了配准精度，并提供了高质量的彩色点云对齐。
