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Logo Ateneo
   
Bayes Filtering
Docente

 

Prof. Sorrenti

Data e luogo

Introduction to Bayes Filtering, with application to Robotic Perception

- DRAFT SCHEDULE

  • Wednesday May 2nd, 2012; 08:30 - 10:30; U14-T014
Basics of probabilities
Basics of Bayes Filtering
  • Monday May 7th, 2012; 08:30 - 10:30; U14-T023
Gaussian Filtering (Kalman)
Extended Kalman Filter
  • Tuesday May 8th, 2012; 08:30 - 11:30; room TBA
laboratory (mlab, simple KF)
  • Monday May 14th, 2012; 08:30 - 10:30; U14-T023
Jacobian-based propagation of uncertainty
EKF for parameter estimation with an implicit output transform
Unscended Kalman Filter
  • Tuesday May 15th, 2012; 08:30 - 11:30; room TBA
laboratory (mlab, more complex example, with EKF)
  • Wednesday May 16th, 2012; 08:30 - 10:30; U14-T014
Information Form Gaussian Filtering
Non parametric filtering
  • Monday May 21st, 2012; 08:30 - 10:30; U14-T023
Non parametric filtering
  • Tuesday May 22nd, 2012; 08:30 - 11:30; room TBA
laboratory (mlab, conclusion of the example, with EKF)
  • Wednesday May 25th, 2012; 08:30 - 10:30; U14-T014
Introduction to the SLAM problem

Robotic Application, TBA

Motivazioni e obiettivi

The course gives an introduction to Bayes Filtering, and presents an application to robotic perception.

Registration

Please send an email to TBA 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)     BAND 6
B    80-89%        (Very Good)      BAND 5
C    70-79%        (Good)           BAND 4
D    60-69%        (Average)        BAND 3
E    50-59%        (Unsatisfactory) BAND 2
F    49% and under (Failure)        BAND 1
Programma

 

Bayes Filtering

Basics of probabilities

Basics of filtering

Gaussian Filtering (Kalman)

Extended Kalman Filter

Jacobian-based propagation of uncertainty EKF for parameter estimation with an implicit output transform Unscended Kalman Filter Information Form Gaussian Filtering Non parametric filtering Non parametric filtering

Modalità di svolgimento

Lecturers

  • Domenico G. Sorrenti will give the classes on Bayes Filtering as well as the corresponding labs.
  • TBA, will give the classes on the robotic application as well as the corresponding labs.

ECTS Notes

Here is our best estimate of the effort required to pass the course by the "average student". This estimate is provided for the students that need an estimate of the effort to get the credits.

  • Introduction to Bayes filtering
  • actual class hours: 12
  • individual study for the class topics: 3
  • actual lab. hours: 9
  • individual working on the lab. assignments: 9
  • total: 12+3+9+9 = 33
  • Robotic application, estimates from the previous instance of the course
  • actual class hours: 9
  • individual study for the class topics: 3
  • actual lab. hours: 6
  • individual working on the lab. assignments: 9
  • total: 9+3+6+9 = 27
Modalità d'esame
Materiale didattico

The course material, beside the references below, is available here; login and password have been communicated during classes. Notice though that most of the "Introduction to Bayes Filtering" part is perfectly covered by the first chapters of the book "Probabilistic Robotics".

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redazioneweb@disco.unimib.it - ultimo aggiornamento di questa pagina 02/05/2012