3D Surveillance Coverage Using Maps Extracted by a Monocular SLAM Algorithm

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 is the knowledge of the terrain's morphology. In this paper, we introduce a two-step centralized procedure to align optimally a swarm of flying vehicles for the aforementioned task. Initially, a single robot constructs a map of the area of interest using a novel monocular-vision-based approach. A state-of-the-art visual-SLAM algorithm tracks the pose of the camera while, simultaneously, building an incremental map of the surrounding environment. The map generated is processed and serves as an input in an optimization procedure using the cognitive adaptive methodology initially introduced in [1], [2]. The output of this procedure is the optimal arrangement of the robot team, which maximizes the monitored area. The efficiency of our approach is demonstrated using real data collected from aerial robots in different outdoor areas.
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https://hal.inria.fr/inria-00625534
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Submitted on : Wednesday, September 21, 2011 - 6:40:41 PM
Last modification on : Tuesday, April 2, 2019 - 2:03:11 AM
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Lefteris Doitsidis, Alessandro Renzaglia, Weiss Stephan, Elias Kosmatopoulos, Davide Scaramuzza, et al.. 3D Surveillance Coverage Using Maps Extracted by a Monocular SLAM Algorithm. IEEE International Conference on Robotics and Intelligent System (IROS), Sep 2011, San Francisco, United States. ⟨inria-00625534⟩

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