Perception in Robotics course T3 2019-2020
Perception in Robotics course, at Skoltech, MS in Data Science, during T3, 2020. About us: we are the Mobile robotics Lab. at Skoltech
This repoository includes all material used during the course: Class notes, unedited videos of the lectures and problem sets.
Link to the YouTube channel with video lectures
- L01: Introduction and Expectation
- L02: Gaussians
- L03: Gaussian II
- L04: Bayes Filter and Kalman Filter
- L05: Motion and Sensor Models
- L06: EKF and Localization
- L07: Particle Filter and Monte-Carlo Localization
- L08: EKF SLAM with known correspondences
- L09: Data Association
- L10: Smoonthing and Mapping (SAM), GraphSLAM
- L11: Squared Root SAM
- L12: Incremental SAM and Pose SLAM
- L13: 3D Poses and RBT
- L14: Point Cloud Aligment
- L15: Mapping
- L16: Visual SLAM
OFFICIAL Telegram channel with TAs
How to use this repo
We will upload regularly all material from canvas to here. Problem Sets will also be uploaded, those requiring code already have the correct structure. Suggestion: This repository could be pushed to your personal space (create new repo) and keep both remotes, here for updates from class, your space to work on problem sets.