TY - GEN
T1 - Multiple UAVs Collaborative Dense Map Construction and Map Fusion
AU - Gao, Chenqi
AU - Lei, Yifei
AU - Hu, Jinwen
AU - Xu, Zhao
AU - Han, Junwei
AU - Su, Yanyu
AU - Pang, Kexin
N1 - Publisher Copyright:
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2025
Y1 - 2025
N2 - To overcome the limitations of single unmanned aerial vehicle (UAV) systems, research has increasingly focused on the coordination of multiple UAV platforms. These platforms are often deployed for tasks requiring advanced environmental perception, where the ability to autonomously generate and fuse dense maps is essential. However, existing algorithms for visual mapping with multiple UAVs exhibit significant shortcomings, including issues with map density, fusion accuracy, and comprehensive system testing. This article addresses these challenges by introducing techniques for dense mapping and map fusion in multiple UAVs systems. We propose a visual dense point cloud mapping algorithm that integrates generalized nearest neighbor iteration with voxel filtering. This method not only reduces redundant map points but also enhances mapping accuracy by mitigating sensor errors and drift that often generate invalid map points and overlaps. Additionally, we introduce a dense map fusion algorithm tailored for overlapping regions. This algorithm establishes precise criteria for map fusion and efficiently achieves dense map fusion by identifying overlapping areas and accurately matching point pairs. The efficacy of the proposed algorithms is demonstrated through experimental validation.
AB - To overcome the limitations of single unmanned aerial vehicle (UAV) systems, research has increasingly focused on the coordination of multiple UAV platforms. These platforms are often deployed for tasks requiring advanced environmental perception, where the ability to autonomously generate and fuse dense maps is essential. However, existing algorithms for visual mapping with multiple UAVs exhibit significant shortcomings, including issues with map density, fusion accuracy, and comprehensive system testing. This article addresses these challenges by introducing techniques for dense mapping and map fusion in multiple UAVs systems. We propose a visual dense point cloud mapping algorithm that integrates generalized nearest neighbor iteration with voxel filtering. This method not only reduces redundant map points but also enhances mapping accuracy by mitigating sensor errors and drift that often generate invalid map points and overlaps. Additionally, we introduce a dense map fusion algorithm tailored for overlapping regions. This algorithm establishes precise criteria for map fusion and efficiently achieves dense map fusion by identifying overlapping areas and accurately matching point pairs. The efficacy of the proposed algorithms is demonstrated through experimental validation.
KW - Dense Construction of the Map
KW - Map Fusion
KW - Multiple Unmanned Aerial Vehicles
UR - http://www.scopus.com/inward/record.url?scp=105006516651&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-2244-3_59
DO - 10.1007/978-981-96-2244-3_59
M3 - 会议稿件
AN - SCOPUS:105006516651
SN - 9789819622436
T3 - Lecture Notes in Electrical Engineering
SP - 620
EP - 630
BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 12
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2024
Y2 - 9 August 2024 through 11 August 2024
ER -