基于虚拟三面体的摄像机与二维激光测距仪外参数最小解标定新算法

胡钊政; 赵斌; 李娜; 夏克文

doi:10.16383/j.aas.2015.c150108

基于虚拟三面体的摄像机与二维激光测距仪外参数最小解标定新算法

doi: 10.16383/j.aas.2015.c150108

胡钊政^1,2, ,,
赵斌²,
李娜³,
夏克文²

1.
武汉理工大学ITS研究中心武汉 430063;
2.
河北工业大学信息工程学院天津 300401;
3.
武汉理工大学自动化学院武汉 430070

基金项目:

国家自然科学基金(51208168),湖北省自然科学基金(2015CFB252),河北省教育厅青年拔尖人才计划(BJ2014-013),武汉市青年科技晨光计划(2015070404010196)资助

详细信息

作者简介:
赵斌河北工业大学信息工程学院硕士研究生.主要研究方向为摄像机与激光外参数标定和优化算法设计.E-mail:terry8120106@aliyun.com

李娜武汉理工大学自动化学院讲师.主要研究方向为电子电路系统和自动控制.E-mail:nal926@whut.edu.cn

夏克文河北工业大学教授.主要研究方向为智能信息处理和优化算法设计.E-mail:kwxia@hebut.edu.cn

通讯作者:
胡钊政武汉理工大学教授.主要研究方向为三维计算机视觉,智能车路系统和主动视觉监控系统.本文通信作者.E-mail:zzhu@whut.edu.cn

计量
- 文章访问数: 1927
- HTML全文浏览量: 79
- PDF下载量: 1582
- 被引次数: 0
出版历程
- 收稿日期: 2015-03-06
- 修回日期: 2015-07-03
- 刊出日期: 2015-11-20

Minimal Solution to Extrinsic Calibration of Camera and 2D Laser Rangefinder Based on Virtual Trihedron

HU Zhao-Zheng^{1,2
, ,},
ZHAO Bin²,
LI Na³,
XIA Ke-Wen²

1.
ITS Research Center, Wuhan University of Technology, Wuhan 430063;
2.
School of Information Engineering, Hebei University of Technology, Tianjin 300401;
3.
School of Automation, Wuhan University of Technology, Wuhan 430070

Funds:

Supported by National Natural Science Foundation of China (51208168), Natural Science Foundation of Hubei Province (2015 CFB252), the Youth Top-Notch Plan of Hebei Department of Education (BJ2014-013), and Wuhan Youth Chenguang Plan (2015070404010196)

摘要

摘要: 摄像机与激光测距仪(Camera and laser rangefinder, LRF)被广泛应用于机器人、移动道路测量车、无人驾驶等领域. 其中, 外参数标定是实现图像与LIDAR数据融合的第一步, 也是至关重要的一步. 本文提出一种新的基于最小解(Minimal solution) 外参数标定算法, 即摄像机与激光仅需对标定棋盘格采集三次数据. 本文首次提出虚拟三面体概念, 并以之构造透视三点问题(Perspective-three-point, P3P)用以计算激光与摄像机之间的坐标转换关系.相对于文献在对偶三维空间(Dual 3D space) 中构造的P3P问题, 本文直接在原始三维空间中构造P3P问题, 具有更直观的几何意义, 更利于对P3P问题进行求解与分析. 针对P3P问题多达八组解的问题, 本文还首次提出一种平面物成像区域约束方法从多解中获取真解, 使得最小解标定法具有更大的实用性与灵活性. 实验中分别利用模拟数据与真实数据对算法进行测试.算法结果表明, 在同等输入的条件下, 本文算法性能超过文献中的算法. 本文所提的平面物成像区域约束方法能从多解中计算出真解, 大大提高了最小解算法的实用性与灵活性.
- 外参数标定 /
- 最小解 /
- 虚拟三面体 /
- 透视三点问题 /
- 多解问题
Abstract: Camera and laser rangefinder (LRF) are widely utilized in various applications, such as robotics, road survey and mapping vehicle, autonomous driving, etc. This paper presents a novel minimal solution based method for extrinsic calibration of a camera and a 2D LRF by introducing a virtual trihedron from 3 input chessboard planes. We formulate the perspective-three-plane (P3P) problem with the virtual trihedron to compute the extrinsic parameters between the camera and the LRF coordinate systems. Compared to the P3P problem in dual 3D space in existing methods, the proposed P3P problem is directly formulated in the primal 3D space, which is more intuitive and straightforward to derive and analyze the solutions. This paper also presents a novel geometric constraint, called restricted area on plane image (RAPI), to derive the unique real solution from up to 8 solutions. Results of both simulation and real data experiment demonstrate that the proposed method outperforms existing methods in terms of the same input image and LIDAR data. Moreover, the proposed method can derive the real solution from multiple solutions, thus making the minimal solution based method more practical and flexible.
- Extrinsic calibration /
- minimal solution /
- virtual trihedron /
- perspective-three-point (P3P) /
- multiple solutions

HTML全文

参考文献(14)

[1]	Kassir A, Peynot T. Reliable automatic camera-laser calibration. In:Proceedings of the 2010 Australasian Conference on Robotics and Automation. Brisbane, Australia:ARAA, 2010. 1-10
[2]	[2] Bosse M, Zlot R, Flick P. Zebedee:design of a spring-mounted 3-D range sensor with application to mobile mapping. IEEE Transactions on Robotics, 2012, 28(5):1104-1119
[3]	[3] Osgood T J, Huang Y P. Calibration of laser scanner and camera fusion system for intelligent vehicles using Nelder-Mead optimization. Measurement Science and Technology, 2013, 24(3):1-10
[4]	[4] Chen Z, Zhuo L, Sun K Q, Zhang C X. Extrinsic calibration of a camera and a laser range finder using point to line constraint. Procedia Engineering, 2012, 29:4348-4352
[5]	[5] Li G H, Liu Y H, Dong L, Cai X P. An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features. In:Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, CA, United states:IEEE, 2007. 3854-3859
[6]	[6] Ha J E. Extrinsic calibration of a camera and laser range finder using a new calibration structure of a plane with a triangular hole. International Journal of Control, Automation and Systems, 2012, 10(6):1240-1244
[7]	[7] Kwak K, Huber D F, Badino H, Kanade T. Extrinsic calibration of a single line scanning LIDAR and a camera. In:Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Francisco, CA, USA:IEEE, 2011. 3283-3289
[8]	[8] Zhang Z Y. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11):1330-1334
[9]	[9] Zhang Q, Pless R. Extrinsic calibration of a camera and laser range finder (improves camera calibration). In:Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems. Sendai, Japan:IEEE, 2004. 2301-2306
[10]	Vasconcelos F, Barreto J P, Nunes U. A minimal solution for the extrinsic calibration of a camera and a laser-rangefinder. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11):2097-2107
[11]	Fishler M A, Bolles R C. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981, 24(6):381-395
[12]	Zhou L P. A new minimal solution for the extrinsic calibration of a 2D LIDAR and a camera using three plane-line correspondences. IEEE Sensors Journal, 2014, 14(2):442-454
[13]	Yang H, Liu X L, Patras I. A simple and effective extrinsic calibration method of a camera and a single line scanning LIDAR. In:Proceedings of the 21st International Conference on Pattern Recognition (ICPR). Tsukuba, Japan:IEEE, 2012. 1439-1442
[14]	Dacorogna B, Marchal P. The role of perspective functions in convexity, poly-convexity, rank-one convexity and separate convexity. Journal of Convex Analysis, 2008, 15(2):271-284