16.485 | Fall 2020 | Graduate
Visual Navigation for Autonomous Vehicles (VNAV)
Course Description
This course covers the mathematical foundations and state-of-the-art implementations of algorithms for vision-based navigation of autonomous vehicles (e.g., mobile robots, self-driving cars, drones). It provides students with a rigorous but pragmatic overview of differential geometry and optimization on manifolds and …
This course covers the mathematical foundations and state-of-the-art implementations of algorithms for vision-based navigation of autonomous vehicles (e.g., mobile robots, self-driving cars, drones). It provides students with a rigorous but pragmatic overview of differential geometry and optimization on manifolds and knowledge of the fundamentals of 2-view and multi-view geometric vision for real-time motion estimation, calibration, localization, and mapping. The theoretical foundations are complemented with hands-on labs based on state-of-the-art mini racecar and drone platforms. It culminates in a critical review of recent advances in the field and a team project aimed at advancing the state of the art.
Learning Resource Types
assignment Programming Assignments
notes Lecture Notes
A drone flying over a terrain and a few other vehicles on that terrain as well
Application of VNAV in DARPA’s Subterranean Challenge to map, navigate, and search complex underground environments, including human-made tunnels, urban underground, and natural cave systems. (Image courtesy of DARPA / public domain.)