Math lasts forever, even in the AI era.
The best way to attack Math is to write it down and think twice yourself.
This course is designed to cover the in-depth math knowledge essential for research in Robotics & State Estimation, with direct applications to:
- Robot perception and navigation
- Simultaneous Localization and Mapping (SLAM)
- Structure from Motion (SfM)
- Visual-Inertial Odometry (VIO)
- Multi-sensor fusion & calibration
| # | Topic | Instructor | Notes |
|---|---|---|---|
| 1 | Basics: probability, linear algebra, linear systems | Yulin Yang | |
| 2 | Rotation, Lie / quaternion, translation, SE(3), SE2(3) | Yulin Yang | — |
| 3 | Camera model, triangulation, calibration, point / line / plane | Yulin Yang | — |
| 4 | Bundle adjustment, vision-based navigation, P3P / PnP, Schur complement | Yulin Yang | — |
| 5 | IMU model, IMU integration | Yulin Yang | — |
| 6 | Kalman filter and batch least squares, marginalization, null space | Yulin Yang | — |
| 7 | State formulation (global-centric, invariant, robocentric, equivariant…) | Chuchu Chen | — |
| 8 | Kalman-filter-based VINS, optimization-based, observability analysis | Chuchu Chen | — |
| 9 | IMU pre-integration-based VINS, multi-IMU | Yulin Yang | — |
| 10 | Learning-based SLAM / Dense Mapping | Xingxing Zuo | — |
- One lecture per week.
- Bring a pen and paper — follow along with the equation derivations.
- Video recordings and notes are shared after each lecture. Please do not redistribute (e.g., to YouTube) for now.
- Discussions and office hours are held on GitHub: https://github.com/yangyulin/rise-tutorial/discussions
Weekly on Sunday:
| Location | Local time |
|---|---|
| Seattle | 6:30 AM – 7:45 AM PDT |
| Washington DC | 9:30 AM – 10:45 AM EDT |
| Abu Dhabi | 5:30 PM – 6:45 PM |
| Beijing | 9:30 PM – 10:45 PM |