Presentation Topic: Taking a Hard Line:  Robot Navigation When Lighting, Weather, and Geometry Won’t Cooperate

Prof. Timothy Barfoot (University of Toronto Robotics Institute) works in the area of autonomy for mobile robots targeting a variety of applications.  He is interested in developing methods (localization, mapping, planning, control) to allow robots to operate over long periods of time in large-scale, unstructured, three-dimensional environments, using rich onboard sensing (e.g., cameras, laser, radar) and computation.  Tim holds a BASc (Aerospace Major) from the UofT Engineering Science program and a PhD from UofT in robotics.  He took up his academic position in May 2007, after spending four years at MDA Robotics (builder of the well-known Canadarm space manipulators), where he developed autonomous vehicle navigation technologies for both planetary rovers and terrestrial applications such as underground mining. He was also a Visiting Professor at the University of Oxford in 2013 and worked as Director of Autonomous Systems at Apple in California in 2017-9.  Tim is an IEEE Fellow, held a Canada Research Chair (Tier 2), was an Early Researcher Awardee in the Province of Ontario, and has received two paper awards at the IEEE International Conference on Robotics and Automation (ICRA 2010, 2021). He is currently the Associate Director of the UofT Robotics Institute, Faculty Affiliate of the Vector Institute, and co-Faculty Advisor of UofT's self-driving car team that won the SAE Autodrive competition five years in a row.  He sits on the Editorial Boards of the International Journal of Robotics Research (IJRR) and IEEE Transactions on Field Robotics (T-FR), the Foundation Board of Robotics: Science and Systems (RSS), and served as the General Chair of Field and Service Robotics (FSR) 2015, which was held in Toronto.  He is the author of a book, State Estimation for Robotics (2017, 2024), which is free to download from his webpage (http://asrl.utias.utoronto.ca/~tdb).

Presentation Topic: Embracing the Power of Non-Gaussian Modeling: A framework for fault detection and integrity monitoring for satellite navigation

Dr. Li-Ta Hsu (Senior Member, IEEE) received the B.S. and Ph.D. degrees in aeronautics and astronautics from National Cheng Kung University, Tainan, Taiwan, in 2007 and 2013, respectively. He is currently an Associate Professor with the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong. He is with Limin Endowed Young Scholar in Aerospace Navigation. He was a Visiting Researcher with the Faculty of Engineering, University College London, London, U.K., and Tokyo University of Marine Science and Technology, Tokyo, Japan, in 2012 and 2013, respectively. He was selected as a Japan Society for the Promotion of Sciences Postdoctoral Fellow with the Institute of Industrial Science, The University of Tokyo, and worked from 2014 to 2016. He is an Associate Fellow of the Royal Institute of Navigation.,Dr. Hsu is a member of ION and serves as a member of the Editorial Board and reviewer in professional journals related to GNSS. In 2013, he received the Student Paper Award and two Best Presentation Awards from the Institute of Navigation (ION).

(To be confirmed) Presentation Topic: Certifiably Correct Machine Perception

Dr. David M. Rosen is an Assistant Professor in the Departments of Electrical and Computer Engineering and Mathematics, and the Khoury College of Computer Sciences (by courtesy). Prior to joining Northeastern, he was a Research Scientist at Oculus Research (now Meta Reality Labs) from 2016 to 2018, and a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) from 2018 to 2021. He is broadly interested in the mathematical and algorithmic foundations of trustworthy autonomy. His research applies tools from nonlinear optimization, differential geometry and topology, abstract algebra, and probability and statistics to devise principled, computationally efficient, and provably robust algorithms for machine perception and control. Much of his recent work has explored the use of principled approximation schemes (such as convex relaxation) to efficiently compute provably-good solutions of challenging machine perception problems (such as SLAM). His work has been recognized with multiple awards at flagship international venues, including the inaugural Best Paper Award at the International Workshop on the Algorithmic Foundations of Robotics (2016), selection as an RSS Pioneer (2019), a Best Student Paper Award at Robotics: Science and Systems (2020), and an Honorable Mention for the IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award (2021).

Presentation Topic: Autonomous Robotic Mapping in Urban Environments

Dr. Hui Kong (Member, IEEE) received the Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, in 2007. He is currently with the State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), the Department of Electromechanical Engineering (EME), and the Department of Computer and Information Science (CIS), University of Macau (UM). Before that, he was affiliated with NJUST, MIT, The Ohio State University, Ecole Normale Superieure (ENS), Paris, France. His research interests include sensing and perception for autonomous driving, mobile robotics, SLAM, and multiview geometry in computer vision. He serves as an Associate Editor for the International Journal of Computer Vision (IJCV) and the International Journal of AI and Autonomous Systems (AIAS).

Presentation Topic: Certifiably Optimal Design and Calibration of infrastructure for Intelligent Vehicle Localization

Dr. Matthew Giamou joined McMaster’s Department of Computing and Software as an Assistant Professor in July of 2023. His Autonomous Robotics and Convex Optimization Laboratory (ARCO Lab) solves challenging robotic perception and planning problems with cutting-edge optimization methods. Prior to joining McMaster, he was a postdoctoral researcher at the Institute for Experiential Robotics at Northeastern University. He received a B.A.Sc. in Engineering Science from the University of Toronto in 2015, an M.Sc. in Aeronautical and Astronautical Engineering from the Massachusetts Institute of Technology in 2017, and a Ph.D. from the University of Toronto’s Institute for Aerospace Studies in 2022. Matthew’s work has been published and presented in multiple international journals and conferences. His core theoretical research interests are all related to global optimization techniques, but his work has been recognized with awards or nominations for applications spanning sensor calibration, multi-robot communication, and deep learning for robot perception. ARCO Lab is presently focused on designing algorithms for global polynomial optimization tailored to spatiotemporal data for fast and robust motion planning and robot perception with performance and safety guarantees.

Presentation Topic: Gaussian Splatting based SLAM

Dr. Hongzhou Yang has over ten years academic and industrial experience regarding navigation and mapping by integrating multiple sensors with various estimation/optimization methods. He obtained Ph.D. in Geomatics Engineering from University of Calgary after proposing and developing a novel real-time precise point positioning (PPP) system in 2018. Passionate about autonomous driving, he worked at the Level 4 autonomous trucking company TuSimple and was responsible for the pose estimation and high-definition map generation. Prior to that, he worked at Profound Positioning Inc. for over four years, researching and developing Level 2 navigation products with low-cost GNSS/IMU/Odometer sensors. His research interests include sensor fusion, localization and mapping, estimation theory, machine learning for navigation, navigation in challenging environment, intelligent traffic system, robotics, and computer vision in pose estimation. He is aiming to explore the power of sensor fusion and machine learning in localization and mapping to improve the accuracy, safety, and reliability of autonomous driving.