  This paper reports on the development, execution, and open-sourcing of a new
robotics course at MIT. The course is a modern take on "Visual Navigation for
Autonomous Vehicles" (VNAV) and targets first-year graduate students and senior
undergraduates with prior exposure to robotics. VNAV has the goal of preparing
the students to perform research in robotics and vision-based navigation, with
emphasis on drones and self-driving cars. The course spans the entire
autonomous navigation pipeline; as such, it covers a broad set of topics,
including geometric control and trajectory optimization, 2D and 3D computer
vision, visual and visual-inertial odometry, place recognition, simultaneous
localization and mapping, and geometric deep learning for perception. VNAV has
three key features. First, it bridges traditional computer vision and robotics
courses by exposing the challenges that are specific to embodied intelligence,
e.g., limited computation and need for just-in-time and robust perception to
close the loop over control and decision making. Second, it strikes a balance
between depth and breadth by combining rigorous technical notes (including
topics that are less explored in typical robotics courses, e.g., on-manifold
optimization) with slides and videos showcasing the latest research results.
Third, it provides a compelling approach to hands-on robotics education by
leveraging a physical drone platform (mostly suitable for small residential
courses) and a photo-realistic Unity-based simulator (open-source and scalable
to large online courses). VNAV has been offered at MIT in the Falls of
2018-2021 and is now publicly available on MIT OpenCourseWare (OCW).
