In the age of self-driving, LiDAR and IMU represent two of the most ubiqui-
tous sensors in use. Kalman filtering and loosely coupled approaches domi-
nate industry techniques, while current research trends towards a more tighly
coupled formulation involving a joint optimization of IMU and LiDAR mea-
surements. After two years of experience working with and creating tightly
coupled LiDAR-inertial odometry (LIO) systems for offroad and indoor envi-
ronments, we detail our findings regarding such implementations. Moreover,
we present a general framework involving point-to-point based registration,
an adaptive robust kernel, and state-of-the-art preintegration for odometry.
Our method operates in real-time on a moderately powerful CPU, and we
showcase its capabilities in high speed offroad environments, as well as indoor
environments.
Furthermore, we provide an extensive section devoted to background in-
formation required to implement our version of LiDAR-inertial odometry. All
algorithms and equations (except for IMU preintegration) are given with the
goal of this document being completely self-contained. We provide robust
analysis of tradeoffs made along the way, and provide direction for future
improvements based on real-world observations on real hardware.
The key contributions were developed over two programs: DARPA RACER,
a program exploring high-speed off-road autonomy across desert and wooded
environments, and MMPUG, a program devoted to indoor navigation with
small mobile robots.