3 papers
cs.RO2026
Balancing Act: Trading Off Odometry and Map Registration for Efficient Lidar Localization
Katya M. Papais, Daniil Lisus, Cedric Le Gentil +2
Most autonomous vehicles rely on accurate and efficient localization, which is achieved by comparing live sensor data to a preexisting map, to navigate their environment. Balancing…
cs.RO2026
Degeneracy-Resilient Teach and Repeat for Geometrically Challenging Environments Using FMCW Lidar
Katya M. Papais, Wenda Zhao, Timothy D. Barfoot
Teach and Repeat (T&R) topometric navigation enables robots to autonomously repeat previously traversed paths without relying on GPS, making it well suited for operations in GPS-de…
cs.RO2026
Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads
Daniil Lisus, Katya M. Papais, Cedric Le Gentil +4
The Boreas Road Trip (Boreas-RT) dataset extends the multi-season Boreas dataset to new and diverse locations that pose challenges for modern autonomous driving algorithms. Boreas-…