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Gregory Dudek

Associate Academic Member
gregory.dudek@mila.quebec
Full Professor and Research Director of Mobile Robotics Lab, McGill University
Vice President and Lab Head of AI Research, Samsung AI Center in Montréal
Google Scholar

Biography

Gregory Dudek is a full professor at McGill University’s CIM which is linked to the School of Computer Science and Research Director of Mobile Robotics Lab. He is also the Lab Director and VP of research at Samsung AI Center Montreal and an Associate academic member at Mila - Quebec Institute of Artificial Intelligence.

Dudek has authored and co-authored over 300 research publications on a wide range of subjects, including visual object description, recognition, RF localization, robotic navigation and mapping, distributed system design, 5G telecommunications and biological perception.

He co-authored the book “Computational Principles of Mobile Robotics” (Cambridge University Press) with Michael Jenkin. He has chaired and been involved in numerous national and international conferences and professional activities concerned with robotics, machine sensing and computer vision.

Dudek’s research interests include perception for mobile robotics, navigation and position estimation, environment and shape modelling, computational vision and collaborative filtering.

Current Students

Farnoosh Faraji
PhD - McGill University
Principal supervisor :
David Meger
farnoosh.faraji@mila.quebec
Github
Google Scholar
Steve Wen
Master's Research - McGill University
Principal supervisor :
Doina Precup
steve.wen@mila.quebec

Publications

Semantic Mapping for View-Invariant Relocalization.
Jimmy Li
David Meger
Gregory Dudek
We propose a system for visual simultaneous localization and mapping (SLAM) that combines traditional local appearance-based features with s… (see more)emantically meaningful object landmarks to achieve both accurate local tracking and highly view-invariant object-driven relocalization. Our mapping process uses a sampling-based approach to efficiently infer the 3D pose of object landmarks from 2D bounding box object detections. These 3D landmarks then serve as a view-invariant representation which we leverage to achieve camera relocalization even when the viewing angle changes by more than 125 degrees. This level of view-invariance cannot be attained by local appearance-based features (e.g. SIFT) since the same set of surfaces are not even visible when the viewpoint changes significantly. Our experiments show that even when existing methods fail completely for viewpoint changes of more than 70 degrees, our method continues to achieve a relocalization rate of around 90%, with a mean rotational error of around 8 degrees.
2019-05-19
2019 International Conference on Robotics and Automation (ICRA) (published)
doi.org
Planning in Dynamic Environments with Conditional Autoregressive Models
Johanna Hansen
Kyle Kastner
Aaron Courville
Gregory Dudek
We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oo… (see more)rd et al., 2017b) for forward planning with MCTS. In order to test this method, we introduce a new environment featuring varying difficulty levels, along with moving goals and obstacles. The combination of high-quality frame generation and classical planning approaches nearly matches true environment performance for our task, demonstrating the usefulness of this method for model-based planning in dynamic environments.
2018-11-24
ArXiv (preprint)
arxiv.org
arxiv.org