2 citations · 2 across the 6 of their papers we have counts for
6 papers
Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching
Kurran Singh, John J. Leonard
Underwater object-level mapping requires incorporating visual foundation models to handle the uncommon and often previously unseen object classes encountered in marine scenarios. I…
LOSS-SLAM: Lightweight Open-Set Semantic Simultaneous Localization and Mapping
Kurran Singh, Tim Magoun, John J. Leonard
Enabling robots to understand the world in terms of objects is a critical building block towards higher level autonomy. The success of foundation models in vision has created the a…
Opti-Acoustic Semantic SLAM with Unknown Objects in Underwater Environments
Kurran Singh, Jungseok Hong, Nicholas R. Rypkema +1
Despite recent advances in semantic Simultaneous Localization and Mapping (SLAM) for terrestrial and aerial applications, underwater semantic SLAM remains an open and largely unadd…
NeuSE: Neural SE(3)-Equivariant Embedding for Long-Term Object-Based Simultaneous Localization and Mapping
Jiahui Fu, Yilun Du, Kurran Singh +2
We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object-based Simultaneous Localization and Mapping (SLAM) for consistent sp…
Robust Change Detection Based on Neural Descriptor Fields
Jiahui Fu, Yilun Du, Kurran Singh +2
The ability to reason about changes in the environment is crucial for robots operating over extended periods of time. Agents are expected to capture changes during operation so tha…
Discrete-Continuous Smoothing and Mapping
Kevin J. Doherty, Ziqi Lu, Kurran Singh +1
We describe a general approach for maximum a posteriori (MAP) inference in a class of discrete-continuous factor graphs commonly encountered in robotics applications. While there a…