SAC-IA Algorithm Based on Parallel KD-Tree Search and Improved Feature Point Selection

Wei Wei
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Abstract

In recent years, with the rapid development of autonomous driving and intelligent robots, point cloud stitching technology has attracted people's attention. At present, the sampling consistency initial registration algorithm is often used for coarse registration of point clouds, but this algorithm has problems such as large randomness in feature point selection and low search efficiency. this paper proposes a feature point selection method considering the relationship between coordinate distance and spatial similarity, which prevents the occurrence of local optimization by setting the distance threshold, and uses the FPFH to calculate the spatial feature similarity relationship between the source point cloud and the target point cloud point pair. The randomly selected feature pairs with similar spatial characteristics are saved to ensure that the selected point pairs are overlapping as much as possible, which is conducive to the solution of rotation matrix and translation vector. By comparing the registration results of the traditional algorithm, it is concluded that the error of the proposed algorithm is reduced by 15.70%. In addition, the KD-Tree parallel technology based on OpenMP is also used to greatly improve the search efficiency of the corresponding point.
基于并行kd树搜索和改进特征点选择的SAC-IA算法
近年来,随着自动驾驶和智能机器人的快速发展,点云拼接技术引起了人们的关注。目前,点云的粗配准通常采用采样一致性初始配准算法,但该算法存在特征点选择随机性大、搜索效率低等问题。本文提出了一种考虑坐标距离与空间相似度关系的特征点选择方法,通过设置距离阈值防止局部优化的发生,并利用FPFH计算源点云和目标点云点对之间的空间特征相似关系。将随机选取的具有相似空间特征的特征对进行保存,保证选取的点对尽可能重叠,有利于旋转矩阵和平移向量的求解。通过与传统配准算法的配准结果进行比较,发现本文算法的配准误差降低了15.70%。此外,还采用了基于OpenMP的KD-Tree并行技术,大大提高了对应点的搜索效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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