测绘通报 ›› 2020, Vol. 0 ›› Issue (4): 21-26.doi: 10.13474/j.cnki.11-2246.2020.0106

• 学术研究 • 上一篇    下一篇

一种基于ISS-SHOT特征的点云配准算法

李宇翔1, 郭际明1, 潘尚毅2, 吕丽丽2, 卢主兴1, 章迪1   

  1. 1. 武汉大学测绘学院, 湖北 武汉 430079;
    2. 中国水利水电第四工程局有限公司, 青海 西宁 810007
  • 收稿日期:2019-07-01 修回日期:2020-03-02 出版日期:2020-04-25 发布日期:2020-05-08
  • 通讯作者: 郭际明。E-mail:jmguo@sgg.whu.edu.cn E-mail:jmguo@sgg.whu.edu.cn
  • 作者简介:李宇翔(1996-),男,硕士生,主要研究方向为点云数据处理。E-mail:lyx_whu@126.com
  • 基金资助:
    国家自然科学基金(41474004);中国电力建设股份有限公司科研项目(KJ-2018-019)

A point cloud registration algorithm based on ISS-SHOT features

LI Yuxiang1, GUO Jiming1, PAN Shangyi2, Lü Lili2, LU Zhuxing1, ZHANG Di1   

  1. 1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
    2. Sinohydro Engineering Bureau;
    4 Co., Ltd., Xining 810007, China
  • Received:2019-07-01 Revised:2020-03-02 Online:2020-04-25 Published:2020-05-08

摘要: 针对点云配准过程中易产生错误对应点、收敛速度慢、配准时间长等问题,提出了一种基于内部形态描述子(ISS)及方向直方图描述子(SHOT)特征的点云配准算法。运用体素格网法下采样后,采用ISS算法提取特征点,并用SHOT对特征点进行描述,利用余弦相似度匹配对应点对,再采用RANSAC算法剔除错误对应点对,使得两片点云获得良好的初始位姿,最后采用点到平面的ICP算法进行精确配准。试验结果表明,与传统ICP算法及基于ISS的SAC-IA+ICP算法相比,本文算法配准精度及配准效率更高,对数据量大、重叠率较低点云具有很好的稳健性。

关键词: ISS, SHOT, 特征描述子, 点云配准, 点到平面ICP

Abstract: Aiming at the problems of error-corresponding points, slow convergence rate and long time for the point cloud registration, a registration algorithm based on theintrinsic shape signatures(ISS) and signature of histograms of orientations(SHOT) is proposed.After down-sampling by voxel grid method, the feature points of point cloud are extracted by the ISS algorithm, described by the SHOT and matched by the cosine similarity. Then, the wrong corresponding points are eliminated by the RANSAC algorithm, so that the two point clouds obtain a good initial positon. Finally, an accurate result can be obtained based on point to plane ICP algorithm.The experimental results show that compared with the traditional ICP algorithm and the ISS-based SAC-IA+ICP algorithm, the proposed algorithm has higher registration accuracy and efficiency, and it is robust to point clouds with large data quantity and low overlap rate.

Key words: ISS, SHOT, feature descriptor, point cloud registration, point to plane ICP

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