6 citations · 21 across the 13 of their papers we have counts for
11 papers · 1 filter
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling
Xin Huang, Guy Rosman, Igor Gilitschenski +4
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicti…
A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM
Jiahui Fu, Qiangqiang Huang, Kevin Doherty +2
In object-based Simultaneous Localization and Mapping (SLAM), 6D object poses offer a compact representation of landmark geometry useful for downstream planning and manipulation ta…
Consensus-Informed Optimization Over Mixtures for Ambiguity-Aware Object SLAM
Ziqi Lu, Qiangqiang Huang, Kevin Doherty +1
Building object-level maps can facilitate robot-environment interactions (e.g. planning and manipulation), but objects could often have multiple probable poses when viewed from a s…
NF-iSAM: Incremental Smoothing and Mapping via Normalizing Flows
Qiangqiang Huang, Can Pu, Dehann Fourie +3
This paper presents a novel non-Gaussian inference algorithm, Normalizing Flow iSAM (NF-iSAM), for solving SLAM problems with non-Gaussian factors and/or non-linear measurement mod…
Advances in Inference and Representation for Simultaneous Localization and Mapping
David M. Rosen, Kevin J. Doherty, Antonio Teran Espinoza +1
Simultaneous localization and mapping (SLAM) is the process of constructing a global model of an environment from local observations of it; this is a foundational capability for mo…
Variational Filtering with Copula Models for SLAM
John D. Martin, Kevin Doherty, Caralyn Cyr +2
The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots. In most cases the shared dependency between these variables is modeled…