结合图像信息的快速点云拼接算法

doi:10.11947/j.AGCS.2016.20140627

测绘学报 ›› 2016, Vol. 45 ›› Issue (1): 96-102.doi: 10.11947/j.AGCS.2016.20140627

结合图像信息的快速点云拼接算法

王瑞岩, 姜光, 高全学

西安电子科技大学通信工程学院综合业务网理论及关键技术国家重点实验室, 陕西西安 710071

收稿日期:2014-12-03 修回日期:2015-08-18 出版日期:2016-01-20 发布日期:2016-01-28
通讯作者: 姜光, gjiang@mail.xidian.edu.cn E-mail:gjiang@mail.xidian.edu.cn
作者简介:王瑞岩(1986—),女,博士生,研究方向为计算机视觉和三维重建。
基金资助:
国家自然科学基金(61271296;61403292);高等学校科学创新引智计划基金(B08038)

Fast Registration Method for Point Clouds Using the Image Information

WANG Ruiyan, JIANG Guang, GAO Quanxue

State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an 710071, China

Received:2014-12-03 Revised:2015-08-18 Online:2016-01-20 Published:2016-01-28
Supported by:
The National Natural Science Foundation of China (Nos. 61271296;61403292);The Program of Introducing Talents of Discipline to Universities (No. B08038)

摘要/Abstract

摘要： 现有三维激光扫描设备通常配有一个同轴相机,它可以对扫描场景进行拍摄。针对带有同轴相机的激光扫描设备,本文提出了一种结合图像信息的快速点云拼接算法。与传统拼接算法同时计算点云间的旋转和平移变换不同,本文对这两种变换分别进行求解。其中,不同扫描点云间的旋转变换是利用视觉几何知识由同轴相机在不同扫描站点下拍摄的图像直接获得,而平移变换是由本文提出的改进ICP 算法得到。在改进的ICP算法中,只有平移变换的3个未知量被迭代计算,其输入是去除旋转变换后的点云。试验结果表明利用图像获得的点云旋转变换具有很高的准确性;并且由于本文算法中迭代过程只针对平移变换的3个变量进行计算,因此与需要迭代计算6个变量的传统ICP算法相比,本文算法计算复杂度大幅降低,同时更易收敛于全局最优值且收敛速度有所提高。

关键词: 点云拼接, 图像信息, 视觉几何, ICP算法

Abstract: On the existing laser scanners, there usually is a coaxial camera, which could capture images in the scanning site. For the laser scanners with a coaxial camera, we propose a fast registration method using the image information. Unlike the traditional registration methods that computing the rotation and translation simultaneously, our method calculates them individually. The rotation transformation between the point clouds is obtained by the knowledge of the vision geometry and the image information, while their translation is acquired by our improved ICP algorithm. In the improved ICP algorithm, only the translation vector is updated iteratively, whose input is the point clouds that removing the rotation transformation. Experimental results show that the rotation matrix obtained by the images has a high accuracy. In addition, compared with the traditional ICP algorithm, our algorithm converges faster and is easier to fall into the global optimum.

Key words: point clouds registration, image information, vision geometry, ICP algorithm

中图分类号:

P237

王瑞岩, 姜光, 高全学. 结合图像信息的快速点云拼接算法[J]. 测绘学报, 2016, 45(1): 96-102.

WANG Ruiyan, JIANG Guang, GAO Quanxue. Fast Registration Method for Point Clouds Using the Image Information[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(1): 96-102.

参考文献

[1] 浦石, 李京伟, 郭四清. 融合语义特征与GPS位置的地面激光点云拼接方法[J]. 测绘学报, 2014, 43(5): 545-550. PU Shi, LI Jingwei, GUO Siqing. Registration of Terrestrial Laser Point Clouds by Fusing Semantic Features and GPS Positions[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(5): 545-550.
[2] BESL P J, MCKAY N D.A Method for Registration of 3-D Shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256.
[3] BLAIS G, LEVINE M D. Registering Multiview Range Data to Create 3D Computer Objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 820-824.
[4] RUSINKIEWICZ S, LEVOY M. Efficient Variants of the ICP Algorithm[C]//Proceedings of the 3rd International Conference on 3-D Digital Imaging and Modeling. Quebec City: IEEE, 2001: 145-152.
[5] BAE K H, LICHT D D. A Method for Automated Registration of Unorganised Point Clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2008, 63(1): 36-54.
[6] AIGER D, MITRA N J, COHEN-OR D. 4-Points Congruent Sets for Robust Pairwise Surface Registration[J]. ACM Transactions on Graphics, 2008, 27(3): 85.
[7] CHEN C S, HUNG Y P, CHENG J B. RANSAC-based DARCES: A New Approach to Fast Automatic Registration of Partially Overlapping Range Images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(11): 1229-1234.
[8] 姚吉利, 马宁, 贾象阳, 等. 光束法区域网平差的地面激光扫描多站点云自动定向方法[J]. 测绘学报, 2014, 43(7): 711-716, 723. YAO Jili, MA Ning, JIA Xiangyang, et al. Auto-registration for Terrestrial Laser Scanning Multi-stations Point Clouds with Bundle Block Adjustment Method[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(7): 711-716, 723.
[9] KANG Zhizhong, LI J, ZHANG Liqiang, et al. Automatic Registration of Terrestrial Laser Scanning Point Clouds Using Panoramic Reflectance Images[J]. Sensors, 2009, 9(4): 2621-2646.
[10] WEINMANN M, JUTZI B. Fully Automatic Image-based Registration of Unorganized TLS Data[J]. International Archives Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011, 38(5/W12): 55-60.
[11] LOWE D G. Distinctive Image Features from Scale-invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[12] 杨化超, 姚国标, 王永波. 基于SIFT的宽基线立体影像密集匹配[J]. 测绘学报, 2011, 40(5): 537-543. YANG Huachao, YAO Guobiao, WANG Yongbo. Dense Matching for Wide Base-line Stereo Images Based on SIFT[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(5): 537-543.
[13] 王晏民, 胡春梅. 一种地面激光雷达点云与纹理影像稳健配准方法[J]. 测绘学报, 2012, 41(2): 266-272. WANG Yanmin, HU Chunmei. A Robust Registration Method for Terrestrial LiDAR Point Clouds and Texture Image[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2): 266-272.
[14] HAN J Y, PERNG N H, CHEN Huangjie. LIDAR Point Cloud Registration by Image Detection Technique[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4): 746-750.
[15] AL-MANASIR K, FRASER C S. Registration of Terrestrial Laser Scanner Data Using Imagery[J]. The Photogrammetric Record, 2006, 21(115): 255-268.
[16] HARTLEY R, ZISSERMAN A. Multiple View Geometry in Computer Vision[M]. Cambridge: Cambridge University Press, 2003.
[17] HARTLEY R. In Defense of the Eight-point Algorithm[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(6): 580-593.
[18] NISTER D. An Efficient Solution to the Five-point Relative Pose Problem[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(6): 756-770.
[19] HORN B K P. Closed-form Solution of Absolute Orientation Using Unit Quaternions[J]. Journal of the Optical Society of America A, 1987, 4(4): 629-642.
[20] ARUN K S, HUANG T S, BLOSTEIN S D. Least-squares Fitting of Two 3-D Point Sets[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(5): 698-700.

结合图像信息的快速点云拼接算法

Fast Registration Method for Point Clouds Using the Image Information

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