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 | - |