Airborne realistic 3D reconstruction based on visual pose correction

doi:10.13474/j.cnki.11-2246.2024.1107

Abstract

Abstract: In urban surveying and mapping, drones equipped with LiDAR hover to collect data are prone to motion distortion due to the influence of fuselage vibration, resulting in poor fusion modeling performance. This article proposes a three-dimensional reconstruction method for airborne laser real scenes based on visual pose correction. The method utilizes video information obtained from binocular cameras during the scanning process of LiDAR to process the trajectory of LiDAR pose changes to correct the pose of the laser point cloud. Then, the corrected laser point cloud is projected onto the coordinate system of a monocular camera, and color information is obtained through collinear equations to achieve information fusion and generate a three-dimensional true color point cloud, and evaluation criteria for four feature dimensions of straightness, flatness, verticality, and fusion coloring are established for the true color point cloud before and after visual pose correction. The experimental results show that the true color point cloud after visual pose correction has a maximum improvement of 77.3% in straightness, 54.5% in flatness, and 54.53° in verticality compared to before correction. The color attachment is significantly more accurate.

Key words: sensors, airborne mobile sensing system, visual pose correction, true color point cloud

CLC Number:

P237

GAO Yunhan, LIN Yili, ZHANG Jinghan, QIAN Wei, XING Yubo, SHI Hang, XIE Yangmin. Airborne realistic 3D reconstruction based on visual pose correction[J]. Bulletin of Surveying and Mapping, 2024, 0(11): 38-43.

References

[1] 李鹏鹏, 孙雅庚.实景三维与多源数据融合技术的研究和应用[J].测绘通报, 2019(S1):133-136.
[2] 王永生, 卢小平, 朱慧, 等.无人机实景三维建模在水利BIM中的应用[J].测绘通报, 2018(3):126-129.
[3] 赵文军, 丁智奇, 王泓森.倾斜实景模型和LiDAR点云数据相融合的不动产测量新方法[J].测绘通报, 2021(2):87-92.
[4] 张子健, 程效军, 曹宇杰, 等.结合激光与视觉点云的古遗迹三维重建应用[J].中国激光, 2020, 47(11):1110001.
[5] YANG B, ALI F, ZHOU B, et al.A novel approach of efficient 3D reconstruction for real scene using unmanned aerial vehicle oblique photogrammetry with five cameras[J].Computers and Electrical Engineering, 2022, 99:107804.
[6] 卢秀山, 俞家勇, 田茂义, 等.车载激光点云与序列化全景影像融合方法[J].中国激光, 2018, 45(5):0510004.
[7] 王铉彬, 李星星, 廖健驰, 等.基于图优化的紧耦合双目视觉/惯性/激光雷达SLAM方法[J].测绘学报, 2022, 51(8):1744-1756.
[8] 俞毓锋, 赵卉菁.基于相机与摇摆激光雷达融合的非结构化环境定位[J].自动化学报, 2019, 45(9):1791-1798.
[9] 谢云鹏, 吕可晶.多源数据融合的城市三维实景建模[J].重庆大学学报, 2022, 45(4):143-154.
[10] 刘华章.机载LiDAR航空摄影测量在三维建模中的应用分析[J].工程建设与设计, 2020(14):247-248.
[11] 查陆九, 夏永华, 汪斌, 等.移动测量的视频影像与激光点云配准方法[J].激光与光电子学进展, 2022, 59(10):1013001.
[12] LIN Y L, FEI Y F, GAO Y H, et al.A LiDAR-Camera calibration and sensor fusion method with edge effect elimination[C]//Proceedings of the 17th International Conference on Control, Automation, Robotics and Vision.Singapore:[s.n.], 2022:28-34.
[13] 施佳豪, 王庆, 冯悠扬.基于三维标定板的相机标定方法[J].传感器与微系统, 2021, 40(6):48-51.
[14] ZHU J, TANG Y, SHAO X, et al.Multi-sensor fusion using fuzzy inference system for a visual-IMU-wheel odometry[J].IEEE Transactions on Instrumentation and Measurement, 2021, 70:250216.
[15] RUBLEE E, RABAUD V, KONOLIGE K, et al.ORB:an efficient alternative to SIFT or SURF[C]//Proceedings of 2011 International Conference on Computer Vision.Barcelona:IEEE, 2011.
[16] BOUGUET J Y.Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm[J].Intel corporation, 2001, 5(1-10):4.
[17] RUSINKIEWICZ S, LEVOY M.Efficient variants of the ICP algorithm[C]//Proceedings of the 3rd International Conference on 3D Digital Imaging and Modeling.Quebec:IEEE, 2002.
[18] HARTLEY R, ZISSERMAN A.Multiple view geometry in computer vision:camera models[M].Cambridge:Cambridge University Press, 2004.
[19] 刘爱东, 黄彬, 卢中武.基于四元数的坐标旋转与插值方法研究[J].计算机与现代化, 2012(2):44-47.