TY - GEN
T1 - 3D Reconstruction Method Based on Autonomous Attitude Estimation for UAVs
AU - Hu, Jinwen
AU - Gao, Chenqi
AU - Xu, Zhao
AU - Lv, Mingwei
N1 - Publisher Copyright:
© Beijing HIWING Scientific and Technological Information Institute 2024.
PY - 2024
Y1 - 2024
N2 - Traditional 3D reconstruction methods usually use the Structure from Motion (SFM) method as the front-end technology, but the SFM usually needs to perform 3D reconstruction after all image data has been collected, which results in a large amount of calculation and a slow speed. The Simultaneous Localization and Mapping (SLAM) method pays more attention to real-time and immediacy, and it can locate and build maps while collecting data in real time. Therefore, this paper proposes a 3D reconstruction method based on visual SLAM for Unmanned Aerial Vehicles (UAVs). In this method, visual SLAM calculates the sparse point cloud map and the attitude information of the UAV. Then using stereo matching, surface reconstruction, and texture reconstruction to perform refined 3D model reconstruction. Based on the above algorithm, this paper designs and implements a system for autonomous positioning and fine mapping of UAVs, and combines it with mixed reality technology to improve the visualization effect of the mapping results. Finally, the effectiveness of the system is verified by experiments.
AB - Traditional 3D reconstruction methods usually use the Structure from Motion (SFM) method as the front-end technology, but the SFM usually needs to perform 3D reconstruction after all image data has been collected, which results in a large amount of calculation and a slow speed. The Simultaneous Localization and Mapping (SLAM) method pays more attention to real-time and immediacy, and it can locate and build maps while collecting data in real time. Therefore, this paper proposes a 3D reconstruction method based on visual SLAM for Unmanned Aerial Vehicles (UAVs). In this method, visual SLAM calculates the sparse point cloud map and the attitude information of the UAV. Then using stereo matching, surface reconstruction, and texture reconstruction to perform refined 3D model reconstruction. Based on the above algorithm, this paper designs and implements a system for autonomous positioning and fine mapping of UAVs, and combines it with mixed reality technology to improve the visualization effect of the mapping results. Finally, the effectiveness of the system is verified by experiments.
KW - 3D reconstruction
KW - UAVs
KW - visual SLAM
UR - https://www.scopus.com/pages/publications/85192165780
U2 - 10.1007/978-981-97-1091-1_35
DO - 10.1007/978-981-97-1091-1_35
M3 - 会议稿件
AN - SCOPUS:85192165780
SN - 9789819710904
T3 - Lecture Notes in Electrical Engineering
SP - 381
EP - 391
BT - Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023) - Volume IV
A2 - Qu, Yi
A2 - Gu, Mancang
A2 - Niu, Yifeng
A2 - Fu, Wenxing
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023
Y2 - 9 September 2023 through 11 September 2023
ER -