基于图像配准的栅格地图拼接方法

祝继华; 周颐; 王晓春; 邗汶锌; 马亮

doi:10.16383/j.aas.2015.c140055

基于图像配准的栅格地图拼接方法

doi: 10.16383/j.aas.2015.c140055

1.
西安交通大学软件学院西安 710049;
2.
西安交通大学广电中心西安 710049

基金项目:

国家自然科学基金(61203326,u1261111),中国博士后科学基金(2012M512004,2013T60878),陕西省自然科学基金(2014JM8342)资助

详细信息

作者简介:
周颐西安交通大学广电中心副编审.主要研究方向为视频和图像处理.E-mail: zhouqinhan@mail.xjtu.edu.cn

通讯作者:
祝继华西安交通大学软件学院副教授.主要研究方向为计算机视觉, 移动机器人和图像处理. 本文通信作者.E-mail: zhujh@mail.xjtu.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2014-01-20
- 修回日期: 2014-05-27
- 刊出日期: 2015-02-20

Grid Map Merging Approach Based on Image Registration

1.
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049;
2.
Radio and Television Center, Xi'an Jiaotong University, Xi'an 710049

Funds:

Supported by National Natural Science Foundation of China (61203326, u1261111), China Postdoctoral Science Foundation (2012M512004, 2013T60878), and Natural Science Foundation of Shaanxi Province of China (2014JM8342)

摘要

摘要: 栅格地图拼接是多移动机器人协同创建环境地图中的一项关键技术. 本文提出一种图像配准意义下的栅格地图拼接方法. 该方法将栅格地图拼接问题视为图像配准问题, 建立相应的目标函数, 并给出局部收敛的迭代最近点算法求解该目标函数. 为获得最优的拼接结果, 该方法从待拼接的地图中提取局部不变特征, 并借助随机抽样一致性算法分析初始拼接参数, 以作为迭代最近点算法的初值. 最后, 提出了拼接参数已知时的栅格地图融合规则. 实验结果表明, 该方法能可靠地实现栅格地图拼接, 且具有精度高和速度快的优点.
- 多移动机器人系统 /
- 栅格地图拼接 /
- 图像配准 /
- 迭代最近点算法 /
- 尺度不变特征转换
Abstract: Grid map merging is an important and fundamental issue in cooperative mapping for multi-robot systems. To address this issue, this paper proposes a novel approach to merging grid maps based on image registration. It first turns the map merging problem into the problem of image registration and then presents the corresponding objective function. This function can be solved by the proposed ICP algorithm, which is locally convergent. To obtain the global minimum, SIFT (scale-invariant feature transform) are extracted from the grid maps and are adopted to estimate good initial parameters by the RANSAC algorithm. Furthermore, a rule is also presented to fuse two grid maps with merging parameters. Experimental results demonstrate that the proposed approach can achieve grid map merging with good robustness, accuracy and efficiency.
- Multiple mobile robot system /
- grid map merging /
- image registration /
- iterative closest point algorithm /
- scale-invariant feature transform (SIFT)

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参考文献(30)

[1]	Durrant-Whyte H, Bailey T. Simultaneous localization and mapping (SLAM): Part I. IEEE Robotics and Automation Magazine, 2006, 13(2): 99-110
[2]	[2] Bailey T, Durrant-Whyte H. Simultaneous localization and mapping (SLAM): Part II. IEEE Robotics and Automation Magazine, 2006, 13(3): 108-117
[3]	[3] Thrun S, Burgard W, Fox D. Probabilistic Robotics. Cambridge: MIT Press, 2005.
[4]	[4] Strasdat H, Montiel J M M, Davison A J. Visual slam: why filter? Image and Vision Computing, 2012, 30(2): 65-77
[5]	Zhu Ji-Hua, Zheng Nan-Ning, Yuan Ze-Jian, Zhang Qiang. A SLAM algorithm based on central difference particle filter. Acta Automatica Sinica, 2010, 6(3): 249-257(祝继华, 郑南宁, 袁泽剑, 张强. 基于中心差分粒子滤波的SLAM算法. 自动化学报, 2010, 6(3): 249-257)
[6]	Song Yu, Li Qing-Ling, Kang Yi-Fei, Yan De-Li. SLAM with square-root cubature Rao-Blackwillised particle filter. Acta Automatica Sinica, 2014, 40(2): 357-367 (宋宇, 李庆玲, 康轶非, 闫德立. 平方根容积Rao-Blackwillised粒子滤波SLAM算法. 自动化学报, 2014, 40(2): 357-367)
[7]	[7] Williams S B, Dissanayake G, Durrant-Whyte H. Towards multi-vehicle simultaneous localisation and mapping. In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation. Washington, D.C., USA: IEEE, 2002, 3: 2743-2748
[8]	[8] Thrun S, Liu Y. Multi-robot SLAM with sparse extended information filers. Robotics Research. Berlin: Springer-Verlag, 2005. 254-266
[9]	[9] Carpin S, Birk A, Jucikas V. On map merging. Robotics and Autonomous Systems, 2005, 53(1): 1-14
[10]	Birk A, Carpin S. Merging occupancy grid maps from multiple robots. Proceedings of the IEEE, 2006, 94(7): 1384- 1397
[11]	Howard A, Parker L E, Sukhatme G S. Experiments with a large heterogeneous mobile robot team: exploration, mapping, deployment and detection. The International Journal of Robotics Research, 2006, 25(5-6): 431-447
[12]	Fox D, Ko J, Konolige K, Limketkai B, Schulz D, Stewart B. Distributed multirobot exploration and mapping. Proceedings of the IEEE, 2006, 94(7): 1325-1339
[13]	Censi A, Iocchi L, Grisetti G. Scan matching in the Hough domain. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain: IEEE, 2005. 2739-2744
[14]	Carpin S. Fast and accurate map merging for multi-robot systems. Autonomous Robots, 2008, 25(3): 305-316
[15]	Saeedi S, Paull L, Trentini M, Seto M, Li H. Map merging using Hough peak matching. In: Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura, Portugal: IEEE, 2012. 4683-4688
[16]	Ma Xin, Song Rui, Guo Rui, Li Yi-Bin. Immune adaptive genetic algorithm for occupancy grid maps merging. Control Theory and Applications, 2009, 26(9): 1004-1008(马昕, 宋锐, 郭睿, 李贻斌. 基于免疫自适应遗传算法的机器人栅格地图融合. 控制理论与应用, 2009, 26(9): 1004-1008)
[17]	Pan Wei, Cai Zi-Xing, Chen Bai-Fan. An approach to cooperative multi-robot map building in complex environments. Journal of Sichuan University (Engineering Science Edition), 2010, 42(1): 144-148(潘薇, 蔡自兴, 陈白帆. 复杂环境下多机器人协作构建地图的方法. 四川大学学报(工程科学版), 2010, 42(1): 144-148)
[18]	Liu Li-Mei, Cai Zi-Xing. Study on map merging for multi-robots. Journal of Chinese Computer Systems, 2012, 33(9): 1934-1937(刘利枚, 蔡自兴. 多机器人地图融合方法研究. 小型微型计算系统, 2012, 33(9): 1934-1937)
[19]	Sun Rong-Chuan, Ma Shu-Gen, Li Bin, Wang Ming-Hui, Wang Yue-Chao. Simultaneous localization and sampled environment mapping based on a divide-and-conquer ideology. Acta Automatica Sinica, 2010, 36(12): 1697-1705(孙荣川, 马书根, 李斌, 王明辉, 王越超. 基于分治法的同步定位与环境采样地图创建. 自动化学报, 2010, 36(12): 1697-1705)
[20]	Besl P J, McKay N D. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256
[21]	Chetverikov D, Stepanov D, Krsek P. Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image and Vision Computing, 2005, 23(3): 299-309
[22]	Nuchter A, Lingemann K, Hertzberg J. Cached k-d tree search for ICP algorithms. In: Proceedings of the 6th International Conference on 3-D Digital Imaging and Modeling. Quebec, Canada: IEEE, 2007. 419-426
[23]	Hwang Y, Han B, Ahn H K. A fast nearest neighbor search algorithm by nonlinear embedding. In: Proceedings of the 2012 IEEE International Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012. 3053-3060
[24]	Nchter A, Elseberg J, Schneider P, Paulus D. Study of parameterizations for the rigid body transformations of the scan registration problem. Computer Vision and Image Understanding, 2010, 114(8): 963-980
[25]	Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
[26]	Lebeda K, Matas J, Chum O. Fixing the locally optimized RANSAC. In: Proceedings of the 23rd British Machine Vision Conference. Guildford, UK: BMVA Press, 2012. 95.1- 95.11
[27]	Brown M, Lowe D G. Automatic panoramic image stitching using invariant features. International Journal of Computer Vision, 2007, 74(1): 59-73
[28]	Zhu Ji-Hua, Zheng Nan-Ning, Yuan Ze-Jian, He Yong-Jian. A SLAM approach by combining ICP algorithm and particle filter. Acta Automatica Sinica, 2009, 35(8): 1107-1113(祝继华, 郑南宁, 袁泽剑, 何永健. 基于ICP算法和粒子滤波的未知环境地图创建. 自动化学报, 2009, 35(8): 1107-1113)
[29]	Ying S H, Peng J G, Du S Y, Qiao H. A scale stretch method based on ICP for 3D data registration. IEEE Transactions on Automation Science and Engineering, 2009, 6(3): 559- 565
[30]	Lu F, Milios E. Robot pose estimation in unknown environments by matching 2D range scans. Journal of Intelligent and Robotic Systems, 1994, 18(3): 249-275