This article surveys recent progress and discusses future opportunities for simultaneous
localization and mapping (SLAM) in extreme underground environments. SLAM in …

This paper reports on the state of the art in underground SLAM by discussing different SLAM
strategies and results across six teams that participated in the three-year-long SubT …

The rapid development of autonomous driving and mobile mapping calls for off-the-shelf
LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various …

Search and rescue with a team of heterogeneous mobile robots in unknown and large-scale
underground environments requires high-precision localization and mapping. This crucial …

We propose Super Odometry, a high-precision multi-modal sensor fusion framework,
providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and …

This paper serves as one of the first efforts to enable large-scale and long-duration
autonomy using the Boston Dynamics Spot robot. Motivated by exploring extreme …

Field robotics in perceptually-challenging environments require fast and accurate state
estimation, but modern LiDAR sensors quickly overwhelm current odometry algorithms. To …

Autonomous exploration of subterranean environments constitutes a major frontier for
robotic systems as underground settings present key challenges that can render robot …

Lidar odometry has attracted considerable attention as a robust localization method for
autonomous robots operating in complex GNSS-denied environments. However, achieving …

We present visual inertial lidar legged navigation system (VILENS), an odometry system for
legged robots based on factor graphs. The key novelty is the tight fusion of four different …