47 citations · 66 across the 9 of their papers we have counts for
10 papers
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…
NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields
Antoni Rosinol, John J. Leonard, Luca Carlone
We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from monocular images. To achieve this, we leverage recent advances…
Probabilistic Volumetric Fusion for Dense Monocular SLAM
Antoni Rosinol, John J. Leonard, Luca Carlone
We present a novel method to reconstruct 3D scenes from images by leveraging deep dense monocular SLAM and fast uncertainty propagation. The proposed approach is able to 3D reconst…
LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments
Yun Chang, Kamak Ebadi, Christopher E. Denniston +9
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 requ…
Smooth Mesh Estimation from Depth Data using Non-Smooth Convex Optimization
Antoni Rosinol, Luca Carlone
Meshes are commonly used as 3D maps since they encode the topology of the scene while being lightweight. Unfortunately, 3D meshes are mathematically difficult to handle directly be…
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
Antoni Rosinol, Andrew Violette, Marcus Abate +5
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 environment at…
Primal-Dual Mesh Convolutional Neural Networks
Francesco Milano, Antonio Loquercio, Antoni Rosinol +2
Recent works in geometric deep learning have introduced neural networks that allow performing inference tasks on three-dimensional geometric data by defining convolution, and somet…
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans
Antoni Rosinol, Arjun Gupta, Marcus Abate +2
We present a unified representation for actionable spatial perception: 3D Dynamic Scene Graphs. Scene graphs are directed graphs where nodes represent entities in the scene (e.g. o…
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
Antoni Rosinol, Marcus Abate, Yun Chang +1
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 and visual…
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
Antoni Rosinol, Torsten Sattler, Marc Pollefeys +1
Visual-Inertial Odometry (VIO) algorithms typically rely on a point cloud representation of the scene that does not model the topology of the environment. A 3D mesh instead offers…