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 …

Recent advances in metric, semantic, and topological mapping have equipped autonomous
robots with concept grounding capabilities to interpret natural language tasks. Leveraging …

Occupancy mapping is a fundamental component of robotic systems to reason about the
unknown and known regions of the environment. This article presents an efficient occupancy …

With the continuous development of robotics and computer vision technology, mobile robots
have been widely applied in various fields. In this process, semantic maps for robots have …

An open problem in mobile manipulation is how to represent objects and scenes in a unified
manner, so that robots can use it both for navigating in the environment and manipulating …

Autonomous exploration of unknown environments using a team of mobile robots demands
distributed perception and planning strategies to enable efficient and scalable performance …

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

As robots become increasingly capable, users will want to describe high-level missions and
have robots fill in the gaps. In many realistic settings, pre-built maps are difficult to obtain, so …

We propose a framework for active mapping and exploration that leverages Gaussian
splatting for constructing information-rich maps. Further, we develop a parallelized motion …

Autonomous robots need to learn the categories of various places by exploring their
environments and interacting with users. However, preparing training datasets with linguistic …