Optimal surveillance coverage for teams of micro aerial vehicles in GPS-denied environments using onboard vision

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• 作者：Lefteris Doitsidis (12) ldoitsidis@chania.teicrete.gr
  Stephan Weiss (3) stephan.weiss@mavt.ethz.ch
  Alessandro Renzaglia (4) alessandro.renzaglia@inria.fr
  Markus W. Achtelik (3) markus.achtelik@mavt.ethz.ch
  Elias Kosmatopoulos (25) kosmatop@ee.duth.gr
  Roland Siegwart (3) rsiegwart@ethz.ch
  Davide Scaramuzza (6) davide.scaramuzza@ieee.org
• 关键词：Mesh map &#8211 ; Mapping &#8211 ; Multi robot coverage &#8211 ; Autonomous micro aerial vehicles
• 刊名：Autonomous Robots
• 出版年：2012
• 出版时间：August 2012
• 年：2012
• 卷：33
• 期：1-2
• 页码：173-188
• 全文大小：2.2 MB
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• 作者单位：1. Department of Electronics, Technological Educational Institute of Crete, Chania, 73133 Greece2. Informatics & Telematics Institute, (ITI-CERTH), 57001 Thessaloniki, Greece3. Autonomous Systems Lab, ETH Zurich, Zurich, Switzerland4. INRIA Rh么ne-Alpes, Grenoble, France5. Dept. of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, 67100 Greece6. AI Lab, University of Zurich, Zurich, Switzerland
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Automation and Robotics
  Electronic and Computer Engineering
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Mechanical Engineering
  Simulation and Modeling
  
• 出版者：Springer Netherlands
• ISSN：1573-7527

文摘

This paper deals with the problem of deploying a team of flying robots to perform surveillance-coverage missions over a terrain of arbitrary morphology. In such missions, a key factor for the successful completion of the mission is the knowledge of the terrain’s morphology. The focus of this paper is on the implementation of a two-step procedure that allows us to optimally align a team of flying vehicles for the aforementioned task. Initially, a single robot constructs a map of the area using a novel monocular-vision-based approach. A state-of-the-art visual-SLAM algorithm tracks the pose of the camera while, simultaneously, autonomously, building an incremental map of the environment. The map generated is processed and serves as an input to an optimization procedure using the cognitive, adaptive methodology initially introduced in Renzaglia et al. (Proceedings of the IEEE international conference on robotics and intelligent system (IROS), Taipei, Taiwan, pp. 3314–3320, 2010). The output of this procedure is the optimal arrangement of the robots team, which maximizes the monitored area. The efficiency of our approach is demonstrated using real data collected from aerial robots in different outdoor areas.