234 citations · 449 across the 21 of their papers we have counts for
22 papers
Hydra++: Real-Time Hierarchical 3D Scene Graph Construction With Object-Level Shape Estimation
Hyungtae Lim, Nathan Hughes, Xihang Yu +5
3D scene graphs provide a hierarchical abstraction of environments by encoding spatial entities, such as objects and places, and their relationships. However, existing scene graph…
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…
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…
Clio: Real-time Task-Driven Open-Set 3D Scene Graphs
Dominic Maggio, Yun Chang, Nathan Hughes +6
Modern tools for class-agnostic image segmentation (e.g., SegmentAnything) and open-set semantic understanding (e.g., CLIP) provide unprecedented opportunities for robot perception…
Khronos: A Unified Approach for Spatio-Temporal Metric-Semantic SLAM in Dynamic Environments
Lukas Schmid, Marcus Abate, Yun Chang +1
Perceiving and understanding highly dynamic and changing environments is a crucial capability for robot autonomy. While large strides have been made towards developing dynamic SLAM…
Kimera2: Robust and Accurate Metric-Semantic SLAM in the Real World
Marcus Abate, Yun Chang, Nathan Hughes +1
We present improvements to Kimera, an open-source metric-semantic visual-inertial SLAM library. In particular, we enhance Kimera-VIO, the visual-inertial odometry pipeline powering…
Foundations of Spatial Perception for Robotics: Hierarchical Representations and Real-time Systems
Nathan Hughes, Yun Chang, Siyi Hu +4
3D spatial perception is the problem of building and maintaining an actionable and persistent representation of the environment in real-time using sensor data and prior knowledge.…
Hydra-Multi: Collaborative Online Construction of 3D Scene Graphs with Multi-Robot Teams
Yun Chang, Nathan Hughes, Aaron Ray +1
3D scene graphs have recently emerged as an expressive high-level map representation that describes a 3D environment as a layered graph where nodes represent spatial concepts at mu…
Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned
Yulun Tian, Yun Chang, Long Quang +4
This paper revisits Kimera-Multi, a distributed multi-robot Simultaneous Localization and Mapping (SLAM) system, towards the goal of deployment in the real world. In particular, th…
D-Lite: Navigation-Oriented Compression of 3D Scene Graphs for Multi-Robot Collaboration
Yun Chang, Luca Ballotta, Luca Carlone
For a multi-robot team that collaboratively explores an unknown environment, it is of vital importance that collected information is efficiently shared among robots in order to sup…
Present and Future of SLAM in Extreme Underground Environments
Kamak Ebadi, Lukas Bernreiter, Harel Biggie +28
This paper reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT co…
LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time Underground 3D Mapping
Andrzej Reinke, Matteo Palieri, Benjamin Morrell +4
Lidar odometry has attracted considerable attention as a robust localization method for autonomous robots operating in complex GNSS-denied environments. However, achieving reliable…
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…
Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM
Christopher E. Denniston, Yun Chang, Andrzej Reinke +5
Multi-robot SLAM systems in GPS-denied environments require loop closures to maintain a drift-free centralized map. With an increasing number of robots and size of the environment,…
Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization
Nathan Hughes, Yun Chang, Luca Carlone
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 nodes represen…
Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems
Yulun Tian, Yun Chang, Fernando Herrera Arias +3
This paper presents Kimera-Multi, the first multi-robot system that (i) is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures resulting f…
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
Ali Agha, Kyohei Otsu, Benjamin Morrell +69
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARP…
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…
Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping
Yun Chang, Yulun Tian, Jonathan P. How +1
We present the first fully distributed multi-robot system for dense metric-semantic Simultaneous Localization and Mapping (SLAM). Our system, dubbed Kimera-Multi, is implemented by…
LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments
Kamak Ebadi, Yun Chang, Matteo Palieri +9
Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, and complex subterranean environments is a challenging problem. Sensors must operate in off-nominal conditions…
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…
DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams
Pierre-Yves Lajoie, Benjamin Ramtoula, Yun Chang +2
To achieve collaborative tasks, robots in a team need to have a shared understanding of the environment and their location within it. Distributed Simultaneous Localization and Mapp…