  State estimation for robots navigating in GPS-denied and
perceptually-degraded environments, such as underground tunnels, mines and
planetary subsurface voids, 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 for the DARPA
Subterranean Challenge, 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, 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.
