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 …

Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost
sensors, the easy fusion of other sensors, and richer environmental information. Traditional …

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments
such as homes or hospitals. Many learning-based approaches have been proposed in …

Large language models (LLMs) have unlocked new capabilities of task planning from
human instructions. However, prior attempts to apply LLMs to real-world robotic tasks are …

This work studies the problem of object goal navigation which involves navigating to an
instance of the given object category in unseen environments. End-to-end learning-based …

3D scene graphs have recently emerged as a powerful high-level representation of 3D
environments. A 3D scene graph describes the environment as a layered graph where …

Reliable and accurate localization and mapping are key components of most autonomous
systems. Besides geometric information about the mapped environment, the semantics …

Humans are able to form a complex mental model of the environment they move in. This
mental model captures geometric and semantic aspects of the scene, describes the …

We provide an open-source C++ library for real-time metric-semantic visual-inertial
Simultaneous Localization And Mapping (SLAM). The library goes beyond existing visual …

In this paper, we present a method for single image three-dimensional (3-D) cuboid object
detection and multiview object simultaneous localization and mapping in both static and …