16 citations · 41 across the 26 of their papers we have counts for
10 papers · 1 filter
Describe Anything Anywhere At Any Moment
Nicolas Gorlo, Lukas Schmid, Luca Carlone
Computer vision and robotics applications ranging from augmented reality to robot autonomy in large-scale environments require spatio-temporal memory frameworks that capture both g…
Advancing AI Challenges for the United States Department of the Air Force
Christian Prothmann, Vijay Gadepally, Jeremy Kepner +35
The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers…
Structured Interfaces for Automated Reasoning with 3D Scene Graphs
Aaron Ray, Jacob Arkin, Harel Biggie +3
In order to provide a robot with the ability to understand and react to a user's natural language inputs, the natural language must be connected to the robot's underlying represent…
Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs
Jared Strader, Aaron Ray, Jacob Arkin +11
In this paper, we introduce a multi-robot system that integrates mapping, localization, and task and motion planning (TAMP) enabled by 3D scene graphs to execute complex instructio…
Gaussian Mapping for Evolving Scenes
Vladimir Yugay, Thies Kersten, Luca Carlone +3
Mapping systems with novel view synthesis (NVS) capabilities, most notably 3D Gaussian Splatting (3DGS), are widely used in computer vision, as well as in various applications, inc…
VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold
Dominic Maggio, Hyungtae Lim, Luca Carlone
We present VGGT-SLAM, a dense RGB SLAM system constructed by incrementally and globally aligning submaps created from the feed-forward scene reconstruction approach VGGT using only…
An Addendum to NeBula: Towards Extending TEAM CoSTAR's Solution to Larger Scale Environments
Ali Agha, Kyohei Otsu, Benjamin Morrell +86
This paper presents an appendix to the original NeBula autonomy solution developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), participating in the DARPA Sub…
ASHiTA: Automatic Scene-grounded HIerarchical Task Analysis
Yun Chang, Leonor Fermoselle, Duy Ta +3
While recent work in scene reconstruction and understanding has made strides in grounding natural language to physical 3D environments, it is still challenging to ground abstract,…
Bayesian Fields: Task-driven Open-Set Semantic Gaussian Splatting
Dominic Maggio, Luca Carlone
Open-set semantic mapping requires (i) determining the correct granularity to represent the scene (e.g., how should objects be defined), and (ii) fusing semantic knowledge across m…
BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenes
Minkyun Seo, Hyungtae Lim, Kanghee Lee +2
Recent advances in deep learning-based point cloud registration have improved generalization, yet most methods still require retraining or manual parameter tuning for each new envi…