Search Results for author:
Found 10 papers, 3 papers with code
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents.
MAAD: A Model and Dataset for "Attended Awareness" in Driving
Our model takes as input scene information in the form of a video and noisy gaze estimates, and outputs visual saliency, a refined gaze estimate, and an estimate of the person's attended awareness.
Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference.
Variational Filtering with Copula Models for SLAM
The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots.
Probabilistic Data Association via Mixture Models for Robust Semantic SLAM
The result is a max-mixture-type model that accounts for multiple data association hypotheses as well as incorrect loop closures.
Robotics
Self-Supervised Visual Place Recognition Learning in Mobile Robots
Place recognition is a critical component in robot navigation that enables it to re-establish previously visited locations, and simultaneously use this information to correct the drift incurred in its dead-reckoned estimate.
Direction-Aware Semi-Dense SLAM
To aide simultaneous localization and mapping (SLAM), future perception systems will incorporate forms of scene understanding.
SLAM with Objects using a Nonparametric Pose Graph
The \textit{data association} and \textit{simultaneous localization and mapping} (SLAM) problems are, individually, well-studied in the literature.
Multi-Volume High Resolution RGB-D Mapping with Dynamic Volume Placement
We present a novel RGB-D mapping system for generating 3D maps over spatially extended regions with higher resolution than current methods using multiple, dynamically placed mapping volumes.
Monocular SLAM Supported Object Recognition
In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis.
