Registration of Urban Aerial Image and LiDAR Based on Line Vectors
Source abstract
Abstract
In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR) data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR) in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS) test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and LiDAR Applications · 3D Surveying and Cultural Heritage · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Tianjin Key Laboratory of Intelligent Information Processing in Remote Sensing, Tianjin 300301, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Jiangsu Province · Open Research Fund of State Key Laboratory of Tianjin Key Laboratory of Intelligent Information Processing in Remote Sensing
References (23)
Show source-provided references
1. Processing of Laser Scanner Data-algorithms and Applications10.1016/S0924-2716(99)00008-8 ↗
2. A Comparison between Photogrammetry and Laser Scanning10.1016/S0924-2716(99)00014-3 ↗
3. Automatic extraction of control points for the registration of optical satellite and LiDAR images10.1109/TGRS.2010.2043677 ↗
4. Integration of LiDAR Data and Optical Multi-view Images for 3D Reconstruction of Building Roofs10.1016/j.optlaseng.2012.10.010 ↗
5. 3D building model reconstruction from multi-view aerial imagery and lidar data10.14358/PERS.77.2.125 ↗
6. Chen, L., Hoang, D., Lin, H., and Nguyen, T. (2016). Innovative Methodology for Multi-View Point Cloud Registration in Robotic 3D Object Scanning and Reconstruction. Appl. Sci., 6.10.3390/app6050132 ↗
7. Gunay, A., Arefi, H., and Hahn, H. (2007, January 23–28). Semi-automatic true orthophoto production by using LIDAR data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.10.1109/IGARSS.2007.4423443 ↗
8. Object Level HSI-LIDAR Data Fusion for Automated Detection of Difficult Targets10.1364/OE.19.020916 ↗
9. Cui, T., Ji, S., Shan, J., Gong, J., and Liu, K. (2017). Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping. Sensors, 17.10.20944/preprints201612.0016.v1 ↗
10. Automatic registration of urban aerial imagery with airborne LiDAR data
11. Orientation of Airborne Laser Scanning Point Clouds with Multi-View, Multi-Scale Image Blocks10.3390/s90806008 ↗
12. Refined Non-rigid Registration of a Panoramic Image Sequence to a LiDAR Point Cloud10.1007/978-3-642-24393-6_7 ↗
13. Automatic registration of UAV-borne sequent images and LiDAR data10.1016/j.isprsjprs.2014.12.025 ↗
14. Automatic registration of optical imagery with statistical similarity 3D LiDAR data using10.1016/j.isprsjprs.2013.11.015 ↗
15. Automatic registration of airborne LiDAR point cloud data and optical imagery depth map based on line and points features10.1016/j.infrared.2015.06.006 ↗
16. Photogrammetric and lidar data registration using linear features10.14358/PERS.71.6.699 ↗
17. Registration of optical images with lidar data and its accuracy assessment10.14358/PERS.79.8.731 ↗
18. Liu, S., Tong, X., Chen, J., Liu, X., Sun, W., Xie, H., Chen, P., Jin, Y., and Ye, Z. (2016). A linear feature-based approach for the registration of unmanned aerial vehicle remotely-sensed images and airborne LiDAR data. Remote Sens., 8.10.3390/rs8020082 ↗
19. A dual quaternion-based, closed-form pairwise registration algorithm for point clouds10.1016/j.isprsjprs.2014.04.013 ↗
20. Fischer, I. (1998). Dual-Number Method in Kinematics, Statics and Dynamics, CRC Press.
21. A comparison of representations of general spatial screw displacement10.1068/b050045 ↗
22. The principal axes decomposition of spatial stiffness matrices10.1109/TRO.2015.2389415 ↗
23. Mikhail, E.M., Bethel, J.S., and McGlone, J.C. (2001). Introduction to Modern Photogrammetry, Jhon Wiley & Sons.