Lectures

2021/09/30: Intro, Sensors

  • Introduction to the course (slides)

  • Robots and Sensors (slides)

(video)

2021/10/03: About Probabilities

  • Introduction to probability (slides)

  • Modeling Dependencies (slides)

  • Dynamic Bayesian Networks (slides)

(video1)(video2)(video3)

2021/10/07: Discrete Filtering

  • Localizing Orazio in a grid world with Bayesian Filter (slides)(source)

(Video1)(Video2)

2021/10/10: Gaussians and Kalman (intro)

2021/10/14: EKF Localization

  • Localizing Orazio in a 2D world with known 2D landmarks using EKF (slides)(source)

2021/10/17: EKF Exercises

  • EKF exercises (pdf)

  • Localizing Orazio in a 2D world with known 2D landmarks observable only through bearing using EKF (slides)(source)(Video)

solution/resample.m

2021/10/21: EKF SLAM (Exercise)

2021/10/24: Data Association

2021/10/28: EKF SLAM (unknown Association)

  • EKF SLAM with unknown known data association (slides)(source)

2021/11/07: Unscented + Differentiation

2021/11/11: Unscented Localization

2021/11/14: Particles and Particle Filters

  • Particle Systems (slides)

  • Filtering Wrapup

2021/11/18: Particles and Particle Filters

2021/11/21: Least Squares

2021/11/25: 3D Stuff

2021/11/28: Least Squares: Uncertainty and Manifolds

  • Modeling Uncertainty in LS (slides)

  • Least Squares on a Manifold (slides)

  • ICP Optimization in 3D on a Manifold (slides)(source)

2021/12/2: More examples on Registration

2021/12/5: Going Visual

slides/probabilistic_robotics_29_course_map.pdf

2021/12/5: Going Visual

2021/12/12: Multivariate Problems

2021/12/16: Factor Graphs

2021/12/19: Closure and Projects

  • Proposed Projects (slides)

  • Wrapup (slides)

  • Exam Samples (and solutions) (pdf)