Lidar odometry has attracted considerable attention as a robust localization
method for autonomous robots operating in complex GNSS-denied environments.
However, achieving reliable and efficient performance on heterogeneous
platforms in large-scale environments remains an open challenge due to the
limitations of onboard computation and memory resources needed for autonomous
operation. In this work, we present LOCUS 2.0, a robust and
computationally-efficient \lidar odometry system for real-time underground 3D
mapping. LOCUS 2.0 includes a novel normals-based \morrell{Generalized
Iterative Closest Point (GICP)} formulation that reduces the computation time
of point cloud alignment, an adaptive voxel grid filter that maintains the
desired computation load regardless of the environment's geometry, and a
sliding-window map approach that bounds the memory consumption. The proposed
approach is shown to be suitable to be deployed on heterogeneous robotic
platforms involved in large-scale explorations under severe computation and
memory constraints. We demonstrate LOCUS 2.0, a key element of the CoSTAR
team's entry in the DARPA Subterranean Challenge, across various underground
scenarios.
We release LOCUS 2.0 as an open-source library and also release a
\lidar-based odometry dataset in challenging and large-scale underground
environments. The dataset features legged and wheeled platforms in multiple
environments including fog, dust, darkness, and geometrically degenerate
surroundings with a total of $11~h$ of operations and $16~km$ of distance
traveled.

本篇研究介绍了 LOCUS 2.0 立体激光雷达测距导航系统，该系统可以实现在复杂的 GNSS 缺失环境中对机器人进行定位和导航，具有高效可靠、节约计算资源等优点，并且适用于大规模环境下进行实时的地下三维建图。