Multi-UAV collaborative system with a feature fast matching algorithm

Tian-miao WANG; Yi-cheng ZHANG; Jian-hong LIANG; Yang CHEN; Chao-lei WANG

doi:10.1631/FITEE.2000047

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Multi-UAV collaborative system with a feature fast matching algorithm

Special Feature on Cooperative Multi-UAV Systems | Updated：2022-05-19

- Multi-UAV collaborative system with a feature fast matching algorithm
- 具有快速匹配特征算法的多无人机协作系统
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 12, Pages: 1695-1712(2020)
- Affiliations：
  
  School of Mechanical Engineering & Automation, Beihang University, Beijing 100191, China
  School of Physics and Mechatronics Engineering, Longyan University, Longyan 364000, China
  State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing 100040, China
- Author bio：
  
  ZHANG Yi-cheng, E-mail: zycet@126.com
- Funds：
- DOI：10.1631/FITEE.2000047
  CLC： TP242.6;V279
- Received：27 January 2020，
  
  Revised：2020-;4-22，
  
  Published Online：04 June 2020，
  
  Published：2020-12
- Accepted：
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Tian-miao WANG, Yi-cheng ZHANG, Jian-hong LIANG, et al. Multi-UAV collaborative system with a feature fast matching algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1695-1712.
DOI：

Tian-miao WANG, Yi-cheng ZHANG, Jian-hong LIANG, et al. Multi-UAV collaborative system with a feature fast matching algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1695-1712. DOI： 10.1631/FITEE.2000047.

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

(log

)优化至

(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。

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

(log

) to

(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.

关键词

Keywords

references

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