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

  • Authors:

  • Lefteris Doitsidis;Stephan Weiss;Alessandro Renzaglia;Markus W. Achtelik;Elias Kosmatopoulos;Roland Siegwart;Davide Scaramuzza

  • Affiliations:

  • Department of Electronics, Technological Educational Institute of Crete, Chania, Greece 73133 and Informatics & Telematics Institute, (ITI-CERTH), Thessaloniki, Greece 57001;Autonomous Systems Lab, ETH Zurich, Zurich, Switzerland;INRIA Rhône-Alpes, Grenoble, France;Autonomous Systems Lab, ETH Zurich, Zurich, Switzerland;Informatics & Telematics Institute, (ITI-CERTH), Thessaloniki, Greece 57001 and Dept. of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece 67100;Autonomous Systems Lab, ETH Zurich, Zurich, Switzerland;AI Lab, University of Zurich, Zurich, Switzerland

  • Venue:
  • Autonomous Robots
  • Year:
  • 2012

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Abstract

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.