**Short description of the course**

The course gives an introduction to Bayes Filtering, and presents an application of Extended Kalman Filtering to vision-based geometric modeling of a scene (a.k.a. SLAM), e.g., for mobile robotics applications. The EKF vision-based SLAM part will be given in consecutive days, so to allow students already skilled in Bayes Filtering to take only this part.

**Registration**

Please send an email to paola dot lembo at disco dot unimib dot it stating that you would like to participate to the course; please specify which part(s) of the course you are going to attend.

**Grading**

Grading will be based on the evaluation given by the lecturers on the quality of:

- the documentation prepared by students on specific course topics;
- the matlab programs that students will be asked to develop during the labs.

The grading scale adopted is:

A 90% and above (Excellence)

B 80-89% (Very Good)

C 70-79% (Good)

D 60-69% (Average)

E 50-59% (Unsatisfactory)

F 49% and under (Failure)