Lecture schedule
Note for current VNAV students at MIT: please refer to CANVAS to find the latest version of the slides below.
| Lecture | Topic | Slides | Notes |
|---|---|---|---|
| 1 | Introduction | - | |
| 2, 3 | Basic 3D Geometry | - | |
| 4, 5 | Lie Groups | - | |
| 6 | Quadrotor Model | ||
| 7 | Quadrotor Controller | - | |
| 8 | Trajectory Optimization (part 1) | ||
| 9, 10 | Trajectory Optimization (part 2) | ||
| 11 | Image Formation | ||
| 12, 13 | Feature Detection and Tracking | ||
| 14 | 2-View Geometry | ||
| 15 | RANSAC | ||
| 16 | From Optimization to Estimation Theory and Back | ||
| 17 | Introduction to Non-Linear Estimation | ||
| 18, 19 | Optimization on Manifolds | - | |
| 20 | Visual and Visual-Inertial Odometry | - | |
| 21 | Place Recognition | - | |
| 22 | Bag-of-Words (BoW) and Object Detection | - | |
| 23 | SLAM I: Formulations and Sparsity | ||
| 24 | SLAM II: Factor Graphs and Marginalization | - | |
| 25, 26 | Advanced Topics: Beyond Cameras | - | |
| 27 | Advanced Topics: Dense 3D Reconstruction | - | |
| 28 | Research Directions in SLAM | - | |
| 29, 30 | Robust Estimation | - | |
| 31 | Deep Learning Architectures on 3D Data | - | |
| 32 | Geometric Deep Learning | - | |
| 33 | GDL and Graph Neural Networks | - |