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LION: Lidar-Inertial Observability-Aware Navigator for Vision-Denied Environments

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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

State estimation for robots navigating in GPS-denied and perceptually-degraded environments, such as underground tunnels, mines and planetary sub-surface voids [1], remains challenging in robotics. Towards this goal, we present LION (Lidar-Inertial Observability-Aware Navigator), which is part of the state estimation framework developed by the team CoSTAR [2] for the DARPA Subterranean Challenge [3], where the team achieved second and first places in the Tunnel and Urban circuits in August 2019 and February 2020, respectively. LION provides high-rate odometry estimates by fusing high-frequency inertial data from an IMU and low-rate relative pose estimates from a lidar via a fixed-lag sliding window smoother. LION does not require knowledge of relative positioning between lidar and IMU, as the extrinsic calibration is estimated online. In addition, LION is able to self-assess its performance using an observability metric that evaluates whether the pose estimate is geometrically ill-constrained. Odometry and confidence estimates are used by HeRO [4], a supervisory algorithm that provides robust estimates by switching between different odometry sources. In this paper we benchmark the performance of LION in perceptually-degraded subterranean environments, demonstrating its high technology readiness level for deployment in the field.

Video: https://youtu.be/Jd-sqBioarI

This research work was partially carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship acknowledged.

A. Tagliabue, J. Tordesillas and X. Cai—Contributed equally.

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Acknowledgments

The authors would like to thank Kasra Khosoussi (ACL-MIT), Benjamin Morrell (JPL) and Kamak Ebadi (JPL) for helpful insights and discussions. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

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Authors and Affiliations

  1. Massachusetts Institute of Technology, Cambridge, MA, USA

    Andrea Tagliabue, Jesus Tordesillas, Xiaoyi Cai, Jonathan P. How & Luca Carlone

  2. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Angel Santamaria-Navarro & Ali-akbar Agha-mohammadi

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  1. Andrea Tagliabue
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  2. Jesus Tordesillas
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  3. Xiaoyi Cai
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  4. Angel Santamaria-Navarro
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  5. Jonathan P. How
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  7. Ali-akbar Agha-mohammadi
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Correspondence to Andrea Tagliabue .

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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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Tagliabue, A. et al. (2021). LION: Lidar-Inertial Observability-Aware Navigator for Vision-Denied Environments. 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_34

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