Full Text:   <1336>

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CLC number: TP242.6; V279

On-line Access: 2020-12-10

Received: 2020-01-27

Revision Accepted: 2020-04-07

Crosschecked: 2020-04-22

Cited: 0

Clicked: 2647

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yi-cheng Zhang

https://orcid.org/0000-0003-3643-9384

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.12 P.1695-1712

http://doi.org/10.1631/FITEE.2000047


Multi-UAV collaborative system with a feature fast matching algorithm


Author(s):  Tian-miao Wang, Yi-cheng Zhang, Jian-hong Liang, Yang Chen, Chao-lei Wang

Affiliation(s):  School of Mechanical Engineering & Automation, Beihang University, Beijing 100191, China; more

Corresponding email(s):   zycet@126.com

Key Words:  Multiple UAVs, Collaboration, Simultaneous localization and mapping (SLAM), Feature description and matching


Tian-miao Wang, Yi-cheng Zhang, Jian-hong Liang, Yang Chen, Chao-lei Wang. Multi-UAV collaborative system with a feature fast matching algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1695-1712.

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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000047"
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Abstract: 
We present a real-time monocular simultaneous localization and mapping (SLAM) system with a new distributed structure for multi-UAV collaboration tasks. The system is different from other general SLAM systems in two aspects: First, it does not aim to build a global map, but to estimate the latest relative position between nearby vehicles; Second, there is no centralized structure in the proposed system, and each vehicle owns an individual metric map and an ego-motion estimator to obtain the relative position between its own map and the neighboring vehicles’. To realize the above characteristics in real time, we demonstrate an innovative feature description and matching algorithm to avoid catastrophic expansion of feature point matching workload due to the increased number of UAVs. Based on the hash and principal component analysis, the matching time complexity of this algorithm can be reduced from O(log N) to O(1). To evaluate the performance, the algorithm is verified on the acknowledged multi-view stereo benchmark dataset, and excellent results are obtained. Finally, through the simulation and real flight experiments, this improved SLAM system with the proposed algorithm is validated.

具有快速匹配特征算法的多无人机协作系统

王田苗1,张以成1,梁建宏1,陈阳2,王超磊3
1北京航空航天大学机械工程及自动化学院,中国北京市,100191
2龙岩大学物理与机电工程学院,中国龙岩市,364000
3北京电子工程总体研究所复杂产品智能制造系统技术国家重点实验室,中国北京市,100040

摘要:针对多无人机协同任务,基于新的分布式结构建立一套实时的单目同步定位与地图创建(SLAM)框架。该SLAM框架与其他一般SLAM框架主要有两点不同:首先它不以建立全局地图为目标,而是着眼于估算无人机最新的相邻位置关系;其次系统中没有中央化结构,每个飞行器拥有独立的计算测量地图和自运动估计器,通过自身地图与相邻无人机地图间的关系计算相对位置。为实时实现以上性能,设计一套新的特征描述与匹配算法,以避免由于无人机数量变多导致的特征数据计算压力灾难性扩张。基于哈希与主成分分析,将匹配算法的时间复杂度从O(log N)优化至O(1)。为评估性能,将算法在多视角的立体数据集上进行验证,取得良好结果。最后,通过仿真与真实飞行试验,测试整体系统可行性。

关键词:多无人机;协作;同步定位与地图创建;特征描述与匹配

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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