High Accuracy Reconstruction of Airborne Streak Tube Imaging LiDAR Using Particle Swarm Optimization
Source abstract
Abstract
Airborne streak tube imaging LiDAR (STIL) consists of several different data-generating subsystems and introduces system errors each time it is installed on an aircraft. These errors change with each installation, which makes the parametric calibration of the LiDAR meaningless. In this study, we propose a high-precision reconstruction method for point clouds that can be used without calibrating the system parameters. In essence, after each remote sensing measurement, a self-checking process is performed with experimental data to replace the fixed system parameters. In this process, the splicing error of the same region measured under different conditions is used as a criterion to optimize the reconstruction parameters via a particle swarm optimization (PSO) algorithm. For a detection distance of 3000 m, the elevation error of the point cloud reconstruction reaches more than 1 m if the placement parameters are not optimized; after optimization, the elevation error can be controlled within 0.3 m.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and LiDAR Applications · 3D Surveying and Cultural Heritage · Advanced Optical Sensing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150080, China
Funding
National Natural Science Foundation of China
References (35)
Show source-provided references
1. High-resolution topography for understanding Earth surface processes: Opportunities and challenges10.1016/j.geomorph.2014.03.008 ↗
2. Huang, J., Stoter, J., Peters, R., and Nan, L.L. (2022). City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds. Remote Sens., 14.10.3390/rs14092254 ↗
3. Urban land cover classification using airborne LiDAR data: A review10.1016/j.rse.2014.11.001 ↗
4. Guo, B., Li, Q.Q., Huang, X.F., and Wang, C.S. (2016). An Improved Method for Power-Line Reconstruction from Point Cloud Data. Remote Sens., 8.10.3390/rs8010036 ↗
5. Li, X., Chen, W.Y., Sanesi, G., and Lafortezza, R. (2019). Remote Sensing in Urban Forestry: Recent Applications and Future Directions. Remote Sens., 11.10.3390/rs11101144 ↗
6. UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA10.1016/j.rse.2017.04.007 ↗
7. Arctic sea ice freeboard retrieval with waveform characteristics for NASA’s Airborne Topographic Mapper (ATM) and Land, Vegetation, and Ice Sensor (LVIS)10.1109/TGRS.2014.2339737 ↗
8. Methodological approaches to infer end-of-winter snow distribution on alpine glaciers10.3189/2013JoG13J015 ↗
9. Area-based vs. tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data10.1016/j.rse.2017.03.017 ↗
10. Airborne lidar change detection: An overview of Earth sciences applications10.1016/j.earscirev.2019.102929 ↗
11. Assessment of the accuracy of DTM river bed model using classical surveying measurement and LiDAR: A case study in Poland10.1080/00396265.2019.1696515 ↗
12. Multiple-slit Streak Tube Imaging Lidar (MS-STIL) Applications10.1117/12.397800 ↗
13. Deblurring streak image of streak tube imaging lidar using Wiener deconvolution filter10.1364/OE.27.037541 ↗
14. Comparison of four deblurring methods for streak image of streak tube imaging lidar10.1364/OSAC.408557 ↗
15. Occluded target detection of streak tube imaging lidar using image inpainting10.1088/1361-6501/abd1b6 ↗
16. Experiments on the range resolution measurement of a slit streak tube imaging lidar10.1016/j.ijleo.2015.07.098 ↗
17. Range accuracy analysis of streak tube imaging lidar systems10.1016/j.optcom.2015.10.020 ↗
18. Gao, J., Sun, J., Wei, J., and Wang, Q. (2011, January 12–16). Research of underwater target detection using a slit streak tube imaging lidar. Proceedings of the 2011 Academic International Symposium on Optoelectronics and Microelectronics Technology, Harbin, China.10.1109/AISMOT.2011.6159363 ↗
19. Experiments of ocean surface waves and underwater target detection imaging using a slit Streak Tube Imaging Lidar10.1016/j.ijleo.2014.05.005 ↗
20. Development of a large-field streak tube for underwater imaging lidar10.1364/AO.464643 ↗
21. Quantitative evaluation of impacts of random errors on ALS accuracy using multiple linear regression method10.1109/TIM.2012.2184190 ↗
22. Random error modeling and analysis of airborne Lidar systems10.1109/TGRS.2013.2277728 ↗
23. Experimental Comparison of Filter Algorithms for Bare-Earth Extraction from Airbore Laser Scanning Point clouds10.1016/j.isprsjprs.2004.05.004 ↗
24. Hu, Y. (2003). Automated Extraction of Digital Terrain Models Roads and Buildings Using Airborne Lidar Data, University of Calgary.
25. Analysis and Recovery of Systematic Errors in Airborne Laser System
26. Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN’95-International Conference on Neural Networks, Perth, WA, Australia.
27. A general robot inverse kinematics solution method based on improved PSO algorithm10.1109/ACCESS.2021.3059714 ↗
28. Training artificial neural networks by a hybrid PSO-CS algorithm10.3390/a8020292 ↗
29. Particle swarm optimization with rotation axis fitting for magnetometer calibration10.1109/TAES.2017.2667458 ↗
30. A novel camera calibration technique based on differential evolution particle swarm optimization algorithm10.1016/j.neucom.2015.03.119 ↗
31. Comprehensive improvement of camera calibration based on mutation particle swarm optimization10.1016/j.measurement.2021.110303 ↗
32. A calibration method of DVL in integrated navigation system based on particle swarm optimization10.1016/j.measurement.2021.110325 ↗
33. Song, X., Yang, B., Feng, Z., Xu, T., Zhu, D., and Jiang, Y. (2009, January 17–19). Camera calibration based on particle swarm optimization. Proceedings of the 2009 2nd International Congress on Image and Signal Processing, Tianjin, China.10.1109/CISP.2009.5302889 ↗
34. Chen, Z., Shao, F., Fan, Z., Wang, X., Dong, C., Dong, Z., Fan, R., and Chen, D. (2023). A Calibration Method for Time Dimension and Space Dimension of Streak Tube Imaging Lidar. Appl. Sci., 13.10.3390/app131810042 ↗
35. Decker, B.L. (1986). World Geodetic System 1984, Defense Mapping Agency Aerospace Center.