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Autonomous MAV-based Indoor Chimney Inspection with 3D Laser Localization and Textured Surface Reconstruction

  • Jan Quenzel
  • Matthias Nieuwenhuisen
  • David Droeschel
  • Marius Beul
  • Sebastian Houben
  • Sven Behnke
Article
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Abstract

Inspection of industrial chimneys and smoke pipes induces high costs due to production downtimes and imposes risks to the health of human workers due to high temperatures and toxic gases. We aim at speeding up and automating this process with multicopter micro aerial vehicles. To acquire high quality sensor data, flying close to the walls of the chimney is inevitable, imposing high demands on good localization and fast and reliable control. In this paper, we present an integrated chimney inspection system based on a small lightweight flying platform, well-suited for maneuvering in narrow space. For navigation and obstacle avoidance, it is equipped with a multimodal sensor setup including a lightweight rotating 3D laser scanner, stereo cameras for visual odometry and high-resolution surface inspection. We tested our system in a decommissioned industrial chimney at the Zollverein UNESCO world heritage site and present results from autonomous flights and reconstructions of the chimney surface.

Keywords

Autonomous inspection SLAM Planning Surface reconstruction 

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Notes

Acknowledgments

The authors wish to thank the Autonomous Systems Lab from ETH Zürich and Ascending Technologies for their technical and organizational support, especially for providing the flying platform. We wish to thank CRN Management GmbH for building the mock-up chimney, for providing expertise for the domain of chimney inspection, and for their support during the evaluation flights. Furthermore, we wish to thank the Zollverein Foundation for the opportunity to develop and evaluate our system at the UNESCO world heritage site Zeche Zollverein.

This work was partially funded by the European Commission in the FP7 project EuRoC (grant 608849) and by the German Research Foundation (DFG) in the project Mapping on Demand (grants BE 2556/7-2 and BE 2556/8-2).

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Autonomous Intelligent Systems GroupUniversity of BonnBonnGermany

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