L
Liam Paull
Researcher at Université de Montréal
Publications - 104
Citations - 3572
Liam Paull is an academic researcher from Université de Montréal. The author has contributed to research in topics: Computer science & Simultaneous localization and mapping. The author has an hindex of 26, co-authored 89 publications receiving 2559 citations. Previous affiliations of Liam Paull include McGill University & University of New Brunswick.
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Journal ArticleDOI
AUV Navigation and Localization: A Review
TL;DR: A review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods, are presented and areas of future research potential are highlighted.
Proceedings ArticleDOI
Duckietown: An open, inexpensive and flexible platform for autonomy education and research
Liam Paull,Jacopo Tani,Heejin Ahn,Javier Alonso-Mora,Luca Carlone,Michal Cap,Yu Fan Chen,Changhyun Choi,Jeff Dusek,Yajun Fang,Daniel Hoehener,Shih-Yuan Liu,Michael Novitzky,Igor Franzoni Okuyama,Jason Pazis,Guy Rosman,Valerio Varricchio,Hsueh-Cheng Wang,Dmitry S. Yershov,Hang Zhao,Michael R. Benjamin,Christopher E. Carr,Maria T. Zuber,Sertac Karaman,Emilio Frazzoli,Domitilla Del Vecchio,Daniela Rus,Jonathan P. How,John J. Leonard,Andrea Censi +29 more
TL;DR: Duckietown is an open, inexpensive and flexible platform for autonomy education and research that comprises small autonomous vehicles built from off-the-shelf components, and cities complete with roads, signage, traffic lights, obstacles, and citizens in need of transportation.
Journal ArticleDOI
A novel domestic electric water heater model for a multi-objective demand side management program
TL;DR: In this article, a hot water heater model is proposed to predict the on/off state of the water heater and the temperature of water in the tank, which enables the development of water usage profiles so that users can be classified based on usage behaviour.
Journal ArticleDOI
Sensor-Driven Online Coverage Planning for Autonomous Underwater Vehicles
TL;DR: In this article, the authors proposed a multobjective optimization approach for underwater mine countermeasure (MCM) surveys using information theory and branch entropy based on a hexagonal cell decomposition.
Proceedings ArticleDOI
Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps
Teddy Ort,Liam Paull,Daniela Rus +2 more
TL;DR: A novel mapless driving framework that combines sparse topological maps for global navigation with a sensor-based perception system for local navigation that allows the vehicle to navigate road networks reliably, and at high speed, without detailed prior maps is addressed.