Generalized Surveying Data Processing: From Geometric Parameters Calculation to Feature Information Extraction
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摘要: 当前，传感器技术、计算机技术和机器人技术迅猛发展，多传感器集成化、智能化趋势越来越明显。工程测量已向自动化、动态化、智能化方向发展，广泛用于大型桥梁、水利枢纽、高铁地铁、高速公路等工程的高精度测量，以及航天、航空、智能制造等领域的精密工业测量。应用领域的拓展也给测量任务提出了新的要求，测量数据处理不再局限于传统的纯几何参数估计，而是逐渐拓展到几何参数和特征信息兼具的广义测量数据处理。回顾了从经典测量数据处理到广义测量数据处理的发展过程，总结多类测量数据的处理分析逻辑，提出大数据时代下的测量数据处理面临的挑战。阐述了广义测量数据处理的基本思路和策略，并以典型案例来进行说明。Abstract: With the rapid development of technologies in sensor, computer and robotics, the trends of multi-sensor integration and intelligent applications are shown in surveying field. Engineering surveying has developed in the direction of automation, dynamism and intelligence, and is widely used for high-precision measurement of large bridges, water conservancy hubs, high-speed rail subways, highways and other projects, as well as precision industrial measurement in aerospace, aviation, intelligent manufacturing and other fields. The expansion of application fields also puts forward new requirements for surveying tasks.The surveying data processing is no longer limited to the traditional pure geometric parameter estimation, but gradually expands to the generalized measurement data processing with both geometric parameters and feature information. We review the development process from classical measurement data processing to generalized measurement data processing, summarize the processing and analysis logic of multiple types of measurement data, and present the challenges of measurement data processing in the era of big data. The basic ideas and strategies of generalized measurement data processing are elaborated and illustrated with several typical cases.
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图 5 知识图谱与自编码器结合的道路病害特征提取框架
Figure 5. Framework to Extract Road Crack by Integrating Knowledge Mapping with Autoencoder
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图 9 基于深度学习技术的管道缺陷识别技术路线图
Figure 9. Technical Scheme of Pipeline Damage Detection Using Deep Learning
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[1] 李清泉. 动态精密工程测量[M]. 北京: 科学出版社, 2021 Li Qingquan. Dynamic Precision Engineering Survey[M]. Beijing: Science Press, 2021
[2] 朱建军, 宋迎春, 胡俊, 等. 测绘大数据时代数据处理理论面临的挑战与发展[J]. 武汉大学学报·信息科学版, 2021, 46(7): 1025-1031 doi: 10.13203/j.whugis20210232 Zhu Jianjun, Song Yingchun, Hu Jun, et al. Challenges and Development of Data Processing Theory in the Era of Surveying and Mapping Big Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7): 1025-1031 doi: 10.13203/j.whugis20210232
[3] Efron B, Hastie T. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science[M]. New York: Cambridge University Press, 2016
[4] 李清泉, 邵成立, 万剑华, 等. 优视摄影测量与泛在实景三维数据采集: 以实景三维青岛为例[J]. 武汉大学学报·信息科学版, 2022, 47(10): 1587-1597 doi: 10.13203/j.whugis20220079 Li Qingquan, Shao Chengli, Wan Jianhua, et al. Optimized Views Photogrammetry and Ubiquitous Real 3D Data Acquisition with the Application Case in Qingdao[J]. Geomatics and Information Science of Wuhan University, 2022, 47(10): 1587-1597 doi: 10.13203/j.whugis20220079
[5] 张永军, 张祖勋, 龚健雅. 天空地多源遥感数据的广义摄影测量学[J]. 测绘学报, 2021, 50(1): 1-11 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202101001.htm Zhang Yongjun, Zhang Zuxun, Gong Jianya. Generalized Photogrammetry of Spaceborne, Airborne and Terrestrial Multi-source Remote Sensing Datasets[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(1): 1-11 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202101001.htm
[6] 高井祥, 王坚, 李增科. 智能背景下测绘科技发展的几点思考[J]. 武汉大学学报·信息科学版, 2019, 44(1): 55-61 doi: 10.13203/j.whugis20180342 Gao Jingxiang, Wang Jian, Li Zengke. Challenges for the Development of Surveying and Mapping Technology in the Age of Intelligence[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 55-61 doi: 10.13203/j.whugis20180342
[7] 张正禄. 工程测量学[M]. 2版. 武汉: 武汉大学出版社, 2013 Zhang Zhenglu. Engineering Surveying[M]. 2nd ed. Wuhan: Wuhan University Press, 2013
[8] 李清泉, 吕世望, 陈智鹏, 等. 冬奥会国家速滑馆超大地坪平整度快速测量[J]. 武汉大学学报·信息科学版, 2022, 47(3): 325-333 doi: 10.13203/j.whugis20210196 Li Qingquan, Lü Shiwang, Chen Zhipeng, et al. Rapid Measurement of Flatness of Oversized Floor of Speed Skating Oval in Winter Olympic Games[J]. Geomatics and Information Science of Wuhan University, 2022, 47(3): 325-333 doi: 10.13203/j.whugis20210196
[9] 李清泉, 黄惠, 姜三, 等. 优视摄影测量方法及精度分析[J]. 测绘学报, 2022, 51(6): 996-1007 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202206018.htm Li Qingquan, Huang Hui, Jiang San, et al. Optimized Views Photogrammetry and Its Precision Analysis[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 996-1007 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202206018.htm
[10] Wang C S, Wang X S, Xu Y P, et al. A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer Interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14
[11] 韦来生, 张伟平. 贝叶斯分析[M]. 合肥: 中国科学技术大学出版社, 2013 Wei Laisheng, Zhang Weiping. Bayesian Analysis[M]. Hefei: University of Science and Technology of China Press, 2013
[12] Xu P L, Shen Y Z, Fukuda Y, et al. Reply to the Comments by K R Koch and J Kusche on Xu et al. (2006) Variance Component Estimation in Linear Inverse Ill-Posed Models, J Geod, 80: 69-81[J]. Journal of Geodesy, 2007, 81(9): 633-635
[13] Wang C S, Ding X L, Li Q Q, et al. Adaptive Regularization of Earthquake Slip Distribution Inversion[J]. Tectonophysics, 2016, 675: 181-195
[14] 谢建, 朱建军. 等式约束对病态问题的影响及约束正则化方法[J]. 武汉大学学报·信息科学版, 2015, 40(10): 1344-1348 doi: 10.13203/j.whugis20130764 Xie Jian, Zhu Jianjun. Influence of Equality Constraints on Ill-Conditioned Problems and Constrained Regularization Method[J]. Geomatics and Information Science of Wuhan University, 2015, 40(10): 1344-1348 doi: 10.13203/j.whugis20130764
[15] 龚健雅. 人工智能时代测绘遥感技术的发展机遇与挑战[J]. 武汉大学学报·信息科学版, 2018, 43(12): 1788-1796 doi: 10.13203/j.whugis20180082 Gong Jianya. Chances and Challenges for Development of Surveying and Remote Sensing in the Age of Artificial Intelligence[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1788-1796 doi: 10.13203/j.whugis20180082
[16] Koller D, Friedman N. Probabilistic Graphical Models: Principles and Techniques[M]. Cambridge, Massachusetts: The MIT Press, 2009
[17] Kschischang F R, Frey B J, Loeliger H A. Factor Graphs and the Sum-Product Algorithm[J]. IEEE Transactions on Information Theory, 2001, 47(2): 498-519
[18] Kaess M, Johannsson H, Roberts R, et al. iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree[J]. The International Journal of Robotics Research, 2012, 31(2): 216-235
[19] 洪永淼, 汪寿阳. 大数据、机器学习与统计学: 挑战与机遇[J]. 计量经济学报, 2021, 1(1): 17-35 https://www.cnki.com.cn/Article/CJFDTOTAL-JLJX202101003.htm Hong Yongmiao, Wang Shouyang. Big Data, Machine Learning and Statistics: Challenges and Opportunities[J]. China Journal of Econometrics, 2021, 1(1): 17-35 https://www.cnki.com.cn/Article/CJFDTOTAL-JLJX202101003.htm
[20] 李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012 Li Hang. Statistical Learning Method[M]. Beijing: Tsinghua University Press, 2012
[21] Ji Q. Probabilistic Graphical Models for Computer Vision[M]. London: Academic Press, 2020
[22] Goodfellow I, Bengio Y, Courville A. Deep Learning[M]. Cambridge, Massachusetts: The MIT Press, 2016
[23] Wang C S, Shu Q Q, Wang X Y, et al. A Random Forest Classifier Based on Pixel Comparison Features for Urban LiDAR Data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 148: 75-86
[24] 王昊奋, 漆桂林, 陈华钧. 知识图谱: 方法、实践与应用[M]. 北京: 电子工业出版社, 2019 Wang Haofen, Qi Guilin, Chen Huajun. Knowledge Graph[M]. Beijing: Publishing House of Electronics Industry, 2019
[25] 李清泉, 朱家松, 李虹, 等. 基于漂流式胶囊机器人的管道快速检测系统[J]. 中国给水排水, 2021, 37(10): 126-132 https://www.cnki.com.cn/Article/CJFDTOTAL-GSPS202110026.htm Li Qingquan, Zhu Jiasong, Li Hong, et al. Rapid Detection System of Pipeline Based on Floating Capsule Robot[J]. China Water and Wastewater, 2021, 37(10): 126-132 https://www.cnki.com.cn/Article/CJFDTOTAL-GSPS202110026.htm
[26] Forster C, Carlone L, Dellaert F, et al. On-Manifold Preintegration for Real-Time Visual: Inertial Odometry[J]. IEEE Transactions on Robotics, 2017, 33(1): 1-21
[27] Qin T, Li P L, Shen S J. VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020
[28] Fang X, Li Q, Zhu J S, et al. Sewer Defect Instance Segmentation, Localization, and 3D Reconstruction for Sewer Floating Capsule Robots[J]. Automation in Construction, 2022, 142: 104494
[29] Li D S, Cong A R, Guo S. Sewer Damage Detection from Imbalanced CCTV Inspection Data Using Deep Convolutional Neural Networks with Hierarchical Classification[J]. Automation in Construction, 2019, 101: 199-208
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