A Hybrid Improved SAC-IA with a KD-ICP Algorithm for Local Point Cloud Alignment Optimization

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS

ACS Applied Bio Materials Pub Date : 2024-07-02 DOI:10.3390/photonics11070635

Yinbao Cheng, Haiman Chu, Yaru Li, Yingqi Tang, Zai Luo, Shaohui Li

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引用次数: 0

Abstract

To overcome incomplete point cloud data obtained from laser scanners scanning complex surfaces, multi-viewpoint cloud data needs to be aligned for use. A hybrid improved SAC-IA with a KD-ICP algorithm is proposed for local point cloud alignment optimization. The scanned point cloud data is preprocessed with statistical filtering, as well as uniform down-sampling. The sampling consistency initial alignment (SAC-IA) algorithm is improved by introducing a dissimilarity vector for point cloud initial alignment. In addition, the iterative closest point (ICP) algorithm is improved by incorporating bidirectional KD-tree to form the KD-ICP algorithm for fine point cloud alignment. Finally, the algorithms are compared in terms of runtime and alignment accuracy. The implementation of the algorithms is based on the Visual Studio 2013 software configurating point cloud library environment for testing experiments and practical experiments. The overall alignment method can be 40%~50% faster in terms of running speed. The improved SAC-IA algorithm provides better transformed poses, combined with the KD-ICP algorithm to select the corresponding nearest neighbor pairs, which improves the accuracy, as well as the applicability of the alignment.

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改进型 SAC-IA 与 KD-ICP 算法混合用于局部点云对齐优化

为了克服激光扫描仪在扫描复杂表面时获得的点云数据不完整的问题，需要对多视角点云数据进行配准。本文提出了一种混合改进 SAC-IA 与 KD-ICP 算法，用于局部点云对齐优化。扫描点云数据经过统计滤波和均匀下采样预处理。通过为点云初始配准引入一个不相似向量，改进了采样一致性初始配准（SAC-IA）算法。此外，还改进了迭代最邻近点（ICP）算法，将双向 KD 树纳入其中，形成了用于精细点云配准的 KD-ICP 算法。最后，比较了这些算法的运行时间和配准精度。算法的实现基于 Visual Studio 2013 软件配置的点云库环境，用于测试实验和实际实验。从运行速度来看，整体配准方法可提高 40%~50% 的速度。改进后的 SAC-IA 算法提供了更好的变换姿态，结合 KD-ICP 算法选择相应的近邻对，提高了配准的准确性和适用性。

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来源期刊

ACS Applied Bio Materials Chemistry-Chemistry (all)

CiteScore

9.40

自引率

2.10%

发文量

464

期刊介绍： ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.