Abstract
With the development of “Digital Earth,” “Reality-Based 3D China,” and “Smart Cities,” technologies such as UAV aerial photography, photogrammetry, LiDAR, oblique photogrammetry, and SLAM are increasingly utilized for constructing reality-based 3D models. However, each individual technology has its limitations in 3D reconstruction, especially in the fine modeling of buildings, such as occlusion in texture mapping and low model accuracy. While oblique photogrammetry captures multi-angle images of terrains, it encounters challenges when acquiring images in concealed locations, resulting in structural deformations and surface artifacts, leading to insufficient model precision and poor elevation accuracy. LiDAR point clouds complement the geometric structure in the blind areas of oblique photogrammetry, resulting in smoother ground surfaces and sharper edges and lines at the base of buildings. Integrating vehicle-mounted LiDAR point clouds with oblique photogrammetry effectively compensates for the limitations of using a single data source for 3D model creation and improves model accuracy. In 3D reconstruction, the fusion of oblique photogrammetry and LiDAR data is crucial. The classical Iterative Closest Point (ICP) algorithm, widely used in point cloud registration, iteratively finds the closest point pairs between two point sets to calculate the transformation matrix, converging to a certain threshold. However, ICP requires a high initial position accuracy of point clouds, and its simple selection of corresponding points based on Euclidean distance may lead to mismatches, impacting the registration precision. As a result, numerous scholars have made improvements and research on this algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zheng X, Chen D, Wang Q et al (2014) Seawater desalination in China: retrospect and prospect. Chem Eng J 242:404–413
Yang B, Ali F, Zhou B et al (2022) A novel approach of efficient 3D reconstruction for real scene using unmanned aerial vehicle oblique photogrammetry with five cameras. Comput Electr Eng 99:107804
Zhang R, Li H, Duan K et al (2020) Automatic detection of earthquake-damaged buildings by integrating UAV oblique photography and infrared thermal imaging. Rem. Sens. 12(16):2621
Wang X, Wang Y, Ma L et al (2020) Information processing technology in the digital protection of architectural cultural heritage. In: 2020 international conference on culture-oriented science and technology (ICCST). IEEE, pp 496–499
Yang L, Li E, Long T et al (2018) A welding quality detection method for arc welding robot based on 3D reconstruction with SFS algorithm. Int J Adv Manufact Technol 94:1209–1220
Kohek S, Strnad D (2018) Interactive large-scale procedural forest construction and visualization based on particle flow simulation. Comput Graph Forum 37(1):389–402
Petrie G (2009) Systematic oblique aerial photography using multiple digital cameras. Photogramm Eng Remote Sens 75(2):102–107
Ge L, Chang HC, Rizos C (2007) Mine subsidence monitoring using multi-source satellite SAR images. Photogramm Eng Remote Sens 73(3):259–266
Wu H, Wang Y, Xing L et al (2021) Application analysis of three-dimensional real scene modeling of substation site based on UAV tilt photography. In: 2021 2nd international conference on intelligent design (ICID). IEEE, pp 220–225
Kim BO, Yun KH, Lee CK (2014) The use of elevation adjusted ground control points for aerial triangulation in coastal areas. KSCE J Civ Eng 18:1825–1830
Zhang J (2010) Multi-source remote sensing data fusion: status and trends. Int J Image Data Fusion 1(1):5–24
Wang R, Zhang Q, Fu CW et al (2019) Underexposed photo enhancement using deep illumination estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 6849–6857
Wunsch P, Hirzinger G (1996) Registration of CAD-models to images by iterative inverse perspective matching. In: Proceedings of 13th international conference on pattern recognition. IEEE, vol 1, pp 78–83
Ulvi A (2021) Documentation, three-dimensional (3D) modelling and visualization of cultural heritage by using unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanners. Int J Remote Sens 42(6):1994–2021
Hu J, You S, Neumann U (2003) Approaches to large-scale urban modeling. IEEE Comput Graph Appl 23(6):62–69
Eker R, Aydın A, Hübl J (2018) Unmanned aerial vehicle (UAV)-based monitoring of a landslide: Gallenzerkogel landslide (Ybbs-Lower Austria) case study. Environ Monit Assess 190:1–14
Oveland I, Hauglin M, Giannetti F et al (2018) Comparing three different ground based laser scanning methods for tree stem detection. Remote Sens 10(4):538
Feng Q, Liu J, Gong J (2015) Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier—a case of Yuyao, China. Water 7(4):1437–1455
Díaz-Varela RA, De la Rosa R, León L et al (2015) High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: application in breeding trials. Rem. Sensing 7(4):4213–4232
Chen Y (2016) Industrial information integration—a literature review 2006–2015. J Ind Inf Integr 2:30–64
Deng T, Zhang K, Shen ZJM (2021) A systematic review of a digital twin city: a new pattern of urban governance toward smart cities. J Manag Sci Eng 6(2):125–134
Ma J, Jiang J (2011) Applications of fault detection and diagnosis methods in nuclear power plants: a review. Prog Nucl Energy 53(3):255–266
Gu X, Wang X, Guo Y (2020) A review of research on point cloud registration methods. IOP Conf Ser Mater Sci Eng 782(2):022070
Lomonte B, Sasa M, Rey-Suárez P et al (2016) Venom of the coral snake Micrurus clarki: proteomic profile, toxicity, immunological cross-neutralization, and characterization of a three-finger toxin. Toxins 8(5):138
Yang J, Li H, Campbell D et al (2015) Go-ICP: a globally optimal solution to 3D ICP point-set registration. IEEE Trans Pattern Anal Mach Intell 38(11):2241–2254
Zhou QY, Park J, Koltun V (2016) Fast global registration. In: Computer vision–ECCV 2016: 14th European conference, Amsterdam, The Netherlands, 11–14 Oct 2016, Proceedings, Part II 14. Springer International Publishing, pp 766–782
Dong Z, Liang F, Yang B et al (2020) Registration of large-scale terrestrial laser scanner point clouds: a review and benchmark. ISPRS J Photogramm Remote Sens 163:327–342
Fu K, Liu S, Luo X et al (2021) Robust point cloud registration framework based on deep graph matching. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8893–8902
Zhang C, Patras P, Haddadi H (2019) Deep learning in mobile and wireless networking: a survey. IEEE Commun Surv Tutor 21(3):2224–2287
Casanova H, Giersch A, Legrand A et al (2014) Versatile, scalable, and accurate simulation of distributed applications and platforms. J Parall Distrib Comput 74(10):2899–2917
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sun, C., Che, G., Dong, X., Zou, R., Feng, L., Ding, X. (2024). Review on Algorithm for Fusion of Oblique Data and Radar Point Cloud. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_58
Download citation
DOI: https://doi.org/10.1007/978-981-99-7502-0_58
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7555-6
Online ISBN: 978-981-99-7502-0
eBook Packages: EngineeringEngineering (R0)