SFly: Swarm of micro flying robots

Markus Achtelik, M. Achtelik, Y. Brunet, M. Chli, S. Chatzichristofis, J. Decotignie, K. Doth, F. Fraundorfer, L. Kneip, Daniel Gurdan, Lionel Heng, E. Kosmatopoulos, L. Doitsidis, Gim Hee Lee, Simon Lynen, Agostino Martinelli, Lorenz Meier, M. Pollefeys, D. Piguet, A. Renzaglia, D. Scaramuzza, R. Siegwart, J. Stumpf, Petri Tanskanen, C. Troiani, S. Weiss
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引用次数: 49

Abstract

The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment.
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SFly:一群微型飞行机器人
SFly项目是一个由欧盟资助的项目,其目标是创造一群自主视觉控制的微型飞行器。其任务是一群MAV自动绘制未知环境,计算最佳监视位置,并将MAV放置在那里,然后在该环境中定位无线电信标。工作范围包括从理论基础到硬件设计和嵌入式编程的多个不同层次的贡献。其中一项贡献是开发了一种新的MAV,一种六轴飞行器,配备了足够的机载计算机视觉处理能力。一个主要的贡献是开发在MAV上实时运行的单目视觉SLAM。视觉SLAM结果与IMU测量结果融合,用于稳定和控制MAV。这使得MAV能够自主飞行,而不需要与地面站建立数据链路。在这个范围内,已经推导出了融合IMU和视觉测量的新颖分析解决方案。除了实时本地SLAM之外,还开发了离线密集映射过程。为此,MAV配备了立体相机系统的有效载荷。利用密集环境图计算MAV群的最优监视位置。为此,提出了一种基于认知自适应优化的优化技术。最后,MAV已经装备了无线电收发器,并且已经开发了一种在观测环境中定位无线电信标的方法。
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