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Radar-Inertial State Estimation and Obstacle Detection for Micro-Aerial Vehicles in Dense Fog

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Experimental Robotics (ISER 2020)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 19))

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Abstract

Disaster response and search and rescue missions are among the most difficult missions in which an autonomous robot can be deployed. These require a robot to autonomously navigate chaotic, unstructured indoor and outdoor environments. However popular state estimation and mapping methods using vision and lidar are severely handicapped by fog, smoke, or other airborne particulates; conditions common in disaster scenarios. This work presents radar-based methods for state estimation and mapping that are not affected by smoke and fog. We demonstrate the performance of these methods are comparable to other popular methods in favorable conditions. We also demonstrate visual and lidar-based methods degrade quickly in fog, while our methods do not.

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Acknowledgements

The authors would like to thank the members of the CoStar team for sharing both their lab space and technical expertise. We would also like to thank Shakeeb Ahmad of the CU Boulder Mechanical Engineering department for his help in demonstrating our radar state estimator’s use in closed-loop control of a micro-aerial vehicle as shown in our video submission.

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Authors and Affiliations

  1. University of Colorado Boulder, Boulder, CO, 80309, USA

    Andrew Kramer & Christoffer Heckman

Authors
  1. Andrew Kramer
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  2. Christoffer Heckman
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Corresponding author

Correspondence to Andrew Kramer .

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Editors and Affiliations

  1. Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy

    Bruno Siciliano

  2. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

    Cecilia Laschi

  3. Department of Computer Science, Stanford University, Stanford, CA, USA

    Oussama Khatib

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Kramer, A., Heckman, C. (2021). Radar-Inertial State Estimation and Obstacle Detection for Micro-Aerial Vehicles in Dense Fog. In: Siciliano, B., Laschi, C., Khatib, O. (eds) Experimental Robotics. ISER 2020. Springer Proceedings in Advanced Robotics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-71151-1_1

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