Bad weather, extreme lighting, and tunnels are just some examples of situations that can challenge our ability to accurately position an autonomous vehicle. Dr. Barfoot will provide a progress update on his group's long-term efforts to produce localization and mapping able to handle such difficult conditions. In particular, he will discuss using deep-learned features to improve camera-based path following for long-term offroad navigation. On the road, they are testing both lidar and radar-based localization in harsh weather conditions to understand the advantages of each. They are also exploring the use of Doppler lidar to carry out egomotion estimation in geometrically degenerate situations including long tunnels. Finally, on the theory side, they have been investigating so-called certifiably optimal algorithms to verify that backend optimization algorithms converge to correct solutions despite poor initial guesses. They hope this work, alongside the contributions of many others, will help move the field down the long tail of edge and corner cases standing in the way of real-world autonomous vehicles.

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 recently completed a leave 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 Field Robotics (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), which is free to download from his webpage (http://asrl.utias.utoronto.ca/~tdb).

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