36 citations · 123 across the 17 of their papers we have counts for
18 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…
A Roadmap for Climate-Relevant Robotics Research
Alan Papalia, Charles Dawson, Laurentiu L. Anton +25
Climate change is one of the defining challenges of the 21st century, and many in the robotics community are looking for ways to contribute. This paper presents a roadmap for clima…
Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation
Xihang Yu, Rajat Talak, Jingnan Shi +3
Modern warehouse automation systems rely on fleets of intelligent robots that generate vast amounts of data -- most of which remains unannotated. This paper develops a self-supervi…
Outlier-Robust Training of Machine Learning Models
Rajat Talak, Charis Georgiou, Jingnan Shi +1
Robust training of machine learning models in the presence of outliers has garnered attention across various domains. The use of robust losses is a popular approach and is known to…
CRISP: Object Pose and Shape Estimation with Test-Time Adaptation
Jingnan Shi, Rajat Talak, Harry Zhang +2
We consider the problem of estimating object pose and shape from an RGB-D image. Our first contribution is to introduce CRISP, a category-agnostic object pose and shape estimation…
KISS-Matcher: Fast and Robust Point Cloud Registration Revisited
Hyungtae Lim, Daebeom Kim, Gunhee Shin +5
While global point cloud registration systems have advanced significantly in all aspects, many studies have focused on specific components, such as feature extraction, graph-theore…
PyPose v0.6: The Imperative Programming Interface for Robotics
Zitong Zhan, Xiangfu Li, Qihang Li +33
PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has…
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training
Jingnan Shi, Rajat Talak, Dominic Maggio +1
Real-world robotics applications demand object pose estimation methods that work reliably across a variety of scenarios. Modern learning-based approaches require large labeled data…
Loc-NeRF: Monte Carlo Localization using Neural Radiance Fields
Dominic Maggio, Marcus Abate, Jingnan Shi +2
We present Loc-NeRF, a real-time vision-based robot localization approach that combines Monte Carlo localization and Neural Radiance Fields (NeRF). Our system uses a pre-trained Ne…
PyPose: A Library for Robot Learning with Physics-based Optimization
Chen Wang, Dasong Gao, Kuan Xu +34
Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physic…
Optimal and Robust Category-level Perception: Object Pose and Shape Estimation from 2D and 3D Semantic Keypoints
Jingnan Shi, Heng Yang, Luca Carlone
We consider a category-level perception problem, where one is given 2D or 3D sensor data picturing an object of a given category (e.g., a car), and has to reconstruct the 3D pose a…
Visual Navigation for Autonomous Vehicles: An Open-source Hands-on Robotics Course at MIT
Luca Carlone, Kasra Khosoussi, Vasileios Tzoumas +5
This paper reports on the development, execution, and open-sourcing of a new robotics course at MIT. The course is a modern take on "Visual Navigation for Autonomous Vehicles" (VNA…
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…
Optimal Pose and Shape Estimation for Category-level 3D Object Perception
Jingnan Shi, Heng Yang, Luca Carlone
We consider a category-level perception problem, where one is given 3D sensor data picturing an object of a given category (e.g. a car), and has to reconstruct the pose and shape o…
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…
ROBIN: a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants
Jingnan Shi, Heng Yang, Luca Carlone
Many estimation problems in robotics, computer vision, and learning require estimating unknown quantities in the face of outliers. Outliers are typically the result of incorrect da…
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…
TEASER: Fast and Certifiable Point Cloud Registration
Heng Yang, Jingnan Shi, Luca Carlone
We propose the first fast and certifiable algorithm for the registration of two sets of 3D points in the presence of large amounts of outlier correspondences. We first reformulate…