机载激光测深数据配准方法比较
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P229.1

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国家自然科学基金(41871381, 41401573);中央级公益性科研院所基本科研业务费专项资金(2015P13);2021年度广东省海洋综合管理专项资金


Comparison of airborne LiDAR bathymetry data registration methods
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    摘要:

    机载激光测深(Airborne LiDAR Bathymetry, ALB)系统可以快速高效地获取海岛礁及其邻近区域的水上水下一体化数据, 但是由于测量区域大部分位于地势变化缓慢的近岸浅水水域, 点云密度低、厚度大, 配准特征稀少, 同名特征提取困难.针对机载激光测深数据的配准研究工作相对较少.本文以我国南海海域的机载激光测深点云为试验对象, 比较基于不同几何特征的ALB点云数据配准方法, 通过配准精度指标对快速点特征直方图(Fast Point Feature Histograms, FPFH)、最长公共子序列(Longest Common Subsequence, LCSS)和广义迭代最近邻点(Generalized Iterative Closest Point, GICP)三种配准方法进行评定.试验结果表明, LCSS线序列方法实现ALB点云数据配准方法的可靠性更高, 能够克服对应特征匹配过程中信息单一以及噪声问题, 提高特征曲线中对应点的稳健估计, 增强航带数据配准的鲁棒性, 是ALB数据配准的一种有效解决方案.

    Abstract:

    Airborne LiDAR Bathymetry (ALB) system can quickly and efficiently obtain the integrated overwater and underwater data of sea islands, reefs and their adjacent areas.However, due to the fact that most of the measurement areas are shallow near-shore waters with slow terrain changes, the obtained point cloud is low in density and large in thickness, resulting in rare registration characteristics.Few studies have been done on the registration of ALB data due to the difficulty in extracting their homonymous features.To address this problem, we employ three registration methods including Fast Point Feature Histograms (FPFH), Longest Common Subsequence (LCSS) and Generalized Iterative Closest Point (GICP) to register the ALB point cloud data in the South China Sea.The registration performance comparison shows that the LCSS line sequence outperforms the other two methods in registration accuracy and reliability.Moreover, the LCSS can tackle the problems of single information and noise in the corresponding feature matching, improve the robust estimation of corresponding points in the feature curve, and enhance the robustness of airstrip data registration.It can be concluded that the LCSS is an effective solution for ALB data registration.

    参考文献
    [1] 郭锴, 刘焱雄, 徐文学, 等. 机载激光测深波形分解中LM与EM参数优化方法比较[J]. 测绘学报, 2020, 49(1): 117-131 GUO Kai, LIU Yanxiong, XU Wenxue, et al. Comparison of LM and EM parameter optimization methods for airborne laser bathymetric full-waveform decomposition[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(1): 117-131
    [2] 王丹菂, 徐青, 邢帅, 等. 机载激光测深去卷积信号提取方法的比较[J]. 测绘学报, 2018, 47(2): 161-169 WANG Dandi, XU Qing, XING Shuai, et al. Comparison of signal extraction method for airborne LiDAR bathymetry based on deconvolution[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(2): 161-169
    [3] Xu W X, Guo K, Liu Y X, et al. Refraction error correction of airborne LiDAR bathymetry data considering sea surface waves[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102402
    [4] Guo K, Xu W X, Liu Y X, et al. Gaussian half-wavelength progressive decomposition method for waveform processing of airborne laser bathymetry[J]. Remote Sensing, 2017, 10(2): 35
    [5] 范强, 刘鹏, 杨俊, 等. 基于3D-Harris与FPFH改进的3D-NDT配准算法[J]. 图学学报, 2020, 41(4): 567-575 FAN Qiang, LIU Peng, YANG Jun, et al. Improved 3D-NDT point cloud registration algorithm based on 3D-Harris and FPFH[J]. Journal of Graphics, 2020, 41(4): 567-575
    [6] 史明霞, 张旭, 张涛. 基于SIFT特征的肺部非刚性配准应用研究[J]. 计算机技术与发展, 2017, 27(11): 181-186 SHI Mingxia, ZHANG Xu, ZHANG Tao. Research on application of pulmonary non-rigid registration method with 3D-SIFT features[J]. Computer Technology and Development, 2017, 27(11): 181-186
    [7] Rusu R B, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration[C]//2009 IEEE International Conference on Robotics and Automation. May 12-17, 2009, Kobe, Japan. IEEE, 2009: 3212-3217
    [8] 童礼华, 程亮, 李满春, 等. 建筑轮廓的车载和航空LiDAR数据配准[J]. 测绘学报, 2013, 42(5): 699-706, 714 TONG Lihua, CHENG Liang, LI Manchun, et al. Registration of vehicle and airborne LiDAR with building contours[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(5): 699-706, 714
    [9] 王永波, 杨化超, 刘燕华, 等. 线状特征约束下基于四元数描述的LiDAR点云配准方法[J]. 武汉大学学报·信息科学版, 2013, 38(9): 1057-1062 WANG Yongbo, YANG Huachao, LIU Yanhua, et al. Linear-feature-constrained registration of LiDAR point cloud via quaternion[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9): 1057-1062
    [10] Cheng X L, Cheng X J, Li Q, et al. Automatic registration of terrestrial and airborne point clouds using building outline features[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(2): 628-638
    [11] 徐景中, 王佳荣. 基于线特征及迭代最近点算法的地基建筑物点云自动配准方法[J]. 计算机应用, 2020, 40(6): 1837-1841 XU Jingzhong, WANG Jiarong. Auto-registration method of ground based building point clouds based on line features and iterative closest point algorithm[J]. Journal of Computer Applications, 2020, 40(6): 1837-1841
    [12] Guo M, Sun M X, Pan D, et al. High-precision detection method for large and complex steel structures based on global registration algorithm and automatic point cloud generation[J]. Measurement, 2021, 172: 108765
    [13] Grant D, Bethel J, Crawford M. Point-to-plane registration of terrestrial laser scans[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 72: 16-26
    [14] 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
    [15] Biber P, Strasser W. The normal distributions transform: a new approach to laser scan matching[C]//Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453). October 27-31, 2003, Las Vegas, NV, USA. IEEE, 2003: 2743-2748
    [16] Okuda H, Kitaaki Y, Hashimoto M, et al. Fast and high-accurate 3D registration algorithm using hierarchical M-ICP[C]//Proceedings of the SPIE, 2005, 6051: 60510N-60510N-8. DOI: 10.1117/12.648811
    [17] Segal A, Haehnel D, Thrun S. Generalized-ICP[C]//Robotics: Science and Systems V. June 28-July 1, 2009, University of Washington, Seattle, USA. 2009
    [18] Lee J D, Hsieh S S, Huang C H, et al. Adaptive dual AK-D tree search algorithm for ICP registration applications[C]//2006 IEEE International Conference on Multimedia and Expo. July 9-12, 2006, Toronto, ON, Canada. IEEE, 2006: 177-180
    [19] Perez-Gonzalez J, Luna-Madrigal F, Piña-Ramirez O. Deep learning point cloud registration based on distance features[J]. IEEE Latin America Transactions, 2019, 17(12): 2053-2060
    [20] Aoki Y, Goforth H, Srivatsan AR point cloud quality improvement[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158: 123-145erence on Computer Vision and Pattern Recognition (CVPR). June 15-20, 2019, Long Beach, CA, USA. IEEE, 2019: 7156-7165
    [21] Kurobe A, Sekikawa Y, Ishikawa K, et al. CorsNet: 3D point cloud registration by deep neural network[J]. IEEE Robotics and Automation Letters, 2020, 5(3): 3960-3966
    [22] Liu W Q, Wang C, Chen S T, et al. Y-Net: learning domain robust feature representation for ground camera image and large-scale image-based point cloud registration[J]. Information Sciences, 2021, 581: 655-677
    [23] Zhou R Q, Li X X, Jiang W S. SCANet: a spatial and channel attention based network for partial-to-partial point cloud registration[J]. Pattern Recognition Letters, 2021, 151: 120-126
    [24] Zhang Z H, Chen G L, Wang X, et al. DDRNet: fast point cloud registration network for large-scale scenes[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 175: 184-198
    [25] Wang Y, Yang B S, Chen Y P, et al. JoKDNet: a joint keypoint detection and description network for large-scale outdoor TLS point clouds registration[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 104: 102534
    [26] Cheng L, Chen S, Liu X Q, et al. Registration of laser scanning point clouds: a review[J]. Sensors, 2018, 18(5): 1641
    [27] Dong Z, Liang F X, Yang B S, et al. Registration of large-scale terrestrial laser scanner point clouds: a review and benchmark[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163: 327-342
    [28] Yang F L, Su D P, Zhang K, et al. Mosaicing of airborne LiDAR bathymetry strips based on Monte Carlo matching[J]. Marine Geophysical Research, 2017, 38(3): 303-311
    [29] Ji X, Yang B S, Tang Q H, et al. A coarse-to-fine strip mosaicing model for airborne bathymetric LiDAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(10): 8129-8142
    [30] Vlachos M, Kollios G, Gunopulos D. Discovering similar multidimensional trajectories[C]//Proceedings 18th International Conference on Data Engineering. February 26-March 1, 2002, San Jose, CA, USA. IEEE, 2002: 673-684
    [31] Vlachos M, Hadjieleftheriou M, Gunopulos D, et al. Indexing multidimensional time-series[J]. The VLDB Journal, 2006, 15(1): 1-20
    [32] 潘荣江, 孟祥旭, 屠长河. 一种基于LCS的物体碎片自动拼接方法[J]. 计算机学报, 2005, 28(3): 350-356 PAN Rongjiang, MENG Xiangxu, TU Changhe. Fragment re-assembly based on LCS matching[J]. Chinese Journal of Computers, 2005, 28(3): 350-356
    [33] Latecki L J, Megalooikonomou V, Wang Q, et al. An elastic partial shape matching technique[J]. Pattern Recognition, 2007, 40(11): 3069-3080
    [34] Vrigkas M, Karavasilis V, Nikou C, et al. Matching mixtures of curves for human action recognition[J]. Computer Vision and Image Understanding, 2014, 119: 27-40
    [35] 王华夏, 程咏梅, 刘楠, 等. 面向地形等高线匹配的三重约束LCSS算法[J]. 西北工业大学学报, 2017, 35(1): 38-42 WANG Huaxia, CHENG Yongmei, LIU Nan, et al. A algorithm based on triple constraint LCSS for terrain contour lines matching[J]. Journal of Northwestern Polytechnical University, 2017, 35(1): 38-42
    [36] 张萍, 李必军, 郑玲, 等. 一种基于改进LCSS的相似轨迹提取方法[J]. 武汉大学学报·信息科学版, 2020, 45(4): 550-556 ZHANG Ping, LI Bijun, ZHENG Ling, et al. A similar trajectory extraction method based on improved LCSS[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 550-556
    [37] Yang B S, Zang Y F. Automated registration of dense terrestrial laser-scanning point clouds using curves[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 95: 109-121
    [38] 彭琳, 刘焱雄, 邓才龙, 等. 机载激光测深系统试点应用研究[J]. 海洋测绘, 2014, 34(4): 35-37, 42 PENG Lin, LIU Yanxiong, DENG Cailong, et al. Experiment of airborne laser bathymetry[J]. Hydrographic Surveying and Charting, 2014, 34(4): 35-37, 42
    [39] Zhang Z, Zhang J Y, Ma Y, et al. Retrieval of nearshore bathymetry around Ganquan island from LiDAR waveform and QuickBird image[J]. Applied Sciences, 2019, 9(20): 4375
    [40] Li J P, Yang B S, Chen C, et al. NRLI-UAV: non-rigid registration of sequential raw laser scans and images for low-cost UAV LiD
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张凡,徐文学,唐玲,王芳,原峰,张敏.机载激光测深数据配准方法比较[J].南京信息工程大学学报(自然科学版),2021,13(6):678-685
ZHANG Fan, XU Wenxue, TANG Ling, WANG Fang, YUAN Feng, ZHANG Min. Comparison of airborne LiDAR bathymetry data registration methods[J]. Journal of Nanjing University of Information Science & Technology, 2021,13(6):678-685

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  • 收稿日期:2021-11-02
  • 在线发布日期: 2022-01-21

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