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: 5491
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.
@article{title="Multi-UAV collaborative system with a feature fast matching algorithm",
author="Tian-miao Wang, Yi-cheng Zhang, Jian-hong Liang, Yang Chen, Chao-lei Wang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="12",
pages="1695-1712",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000047"
}
%0 Journal Article
%T Multi-UAV collaborative system with a feature fast matching algorithm
%A Tian-miao Wang
%A Yi-cheng Zhang
%A Jian-hong Liang
%A Yang Chen
%A Chao-lei Wang
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 12
%P 1695-1712
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000047
TY - JOUR
T1 - Multi-UAV collaborative system with a feature fast matching algorithm
A1 - Tian-miao Wang
A1 - Yi-cheng Zhang
A1 - Jian-hong Liang
A1 - Yang Chen
A1 - Chao-lei Wang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 12
SP - 1695
EP - 1712
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000047
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.
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