17th International Symposium on Experimental Robotics - ISER 2020, Valletta (Malta). 15-18 November 2021
Summary:
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.
DOI: 
https://doi.org/10.1007/978-3-030-71151-1_34
Published in Experimental Robotics, pp: 380-390, ISBN: 978-3-030-71150-4
Publication date: 2021-03-28.
Citation:
A. Tagliabue, J. Tordesillas Torres, X. Cai, A. Santamaria-Navarro, J.P. How, L. Carlone, A. Agha-mohammadi, LION: Lidar-Inertial Observability-Aware navigator for vision-denied environments, 17th International Symposium on Experimental Robotics - ISER 2020, Valletta (Malta). 15-18 November 2021. In: Experimental Robotics: The 17th International Symposium, ISBN: 978-3-030-71150-4
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