Probabilistic Robotics (UB-2016)

Brief Course Description
Probabilistic techniques for robot localization and mapping.  Bayesian estimation.

Table of Contents
  1. Introduction. (1x1 slides pdf)
  2. Bayesian Filtering. (2x1 slides pdf)
  3. Histogram Filter and Grid Localization. (2x1 slides pdf)
  4. Particle Filter and Montecarlo Localization. (2x1 slides pdf)
  5. Kalman Filter  (2x1 slides pdfand Map based Localization (2x1 slides pdf).
  1. List of Exercices (pdf).
  2. List of Solutions (pdf).

MATLAB assignments. All of them contain a description document and a MATLAB script to be completed. All the Lab assignments are based on solving the localization problem from the course example: "Monobot Moving in a Hallway". To run the animation it is necessary to download an animation (Download the OSX/Linux Version, Download the Windows Version).
  1. Grid Localization. (download)
  2. Particle Filter Localization. (download) (If your MATLAD does not have the pdf function available, download the alternative normpdf.m function)
  3. Kalman Filter Localization. (download)