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.
- 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
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.