Introduction to the course (slides)
Robots and Sensors (slides)
(Video)
Introduction to Probabilities (slides)
Modeling Dependencies (slides)
(Video1)(Video2)
Dynamic Bayesian Networks and Intro to filtering (slides)
Grid Localization with Orazio (slides)(source)
Gaussian (slides)
Exercise (pdf)
Extended Kalman Filter (slides)(Video)
Exercises, on paper (pdf)(Video1, Video2, Video3)
EKF for Localization (slides)(source)
(Video1)(Video2)(Video3)
Localization Bearing Only(slides)(source)(Video)
Planar landmark based SLAM (slides)(source)(Video)
Planar landmark based SLAM (slides)
Planar landmark based SLAM (slides)(source)
(Video) Due o network issues we linked the video of last year. We will do a Q&A session at a link you will receive via the mailing list on Nov 2@ 13:00
Unscented Transform and UKF (slides)
Exercise (slides)(source)
Particle filters (slides)
Neighbors (slides) (source distance map) (source, kd_tree)
Differentiation (slides)(source)
Blackboard Lesson
Least Squares and 2D point Alignment (slides) (source)
Exercise (slides) (source)
3D Point registration (source)
Linear Relaxation (source)
(slides)(Video)
M Estimators (slides)
(Video) due to network issues the streaming was interrupted. The video is from last year.
Fractions of the video for this year are here(Video1)(Video2)
Uncertainty in Least Squares (slides)
LS on a Manifolds (slides)
ICP Optimization (slides) (source)
similarities, direction (slides) (source)
Complete example with data association in C++ (slides)(source)
Triangulation, Epipolar Constraint, Essential and Fundamental matrix, 8 pt Algorithm (slides) (source)
Random Sample Consensus (slides)
MultiPoint Registration (slides)(source)
Multipose Registration (slides)
Bundle Adjustment (source)
Spherical Bundle Adjustment (source)
Factor Graphs (slides)
Total Least Squares (source)
Course Map (slides)
Projects (slides)
Exams (example1) (example2)