Active simultaneous localization and mapping (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …

Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead
of manually designing costs based on terrain features, existing methods learn terrain …

A key challenge in off-road navigation is that even visually similar terrains or ones from the
same semantic class may have substantially different traction properties. Existing work …

Motion planning in off-road environments re-quires reasoning about both the geometry and
semantics of the scene (eg, a robot may be able to drive through soft bushes but not a fallen …

Autonomous robot operation in unstructured and unknown environments requires efficient
techniques for mapping and exploration using streaming range and visual observations …

In order for a mobile robot to be able to effectively operate in complex, dynamic
environments it must be capable of understanding both where and what the objects around …

Traditional approaches for active mapping focus on building geometric maps. For most real-
world applications, however, actionable information is related to semantically meaningful …

The lack of communication between agents in a multi-robot system is often regarded as a
limiting factor that can affect and delay cooperative exploration and exploitation of cluttered …

This paper develops a real-time decentralized metric-semantic Simultaneous Localization
and Mapping (SLAM) approach that leverages a sparse and lightweight object-based …

Exploration of unknown space with an autonomous mobile robot is a well-studied problem.
In this work we broaden the scope of exploration, moving beyond the pure geometric goal of …