Site of the previous year (2021/22) 

News

• 2023-02-21: we closed the past exam on infostud upon request of the administration. The students who did not complete the exam (project pending/written test pending) PRESERVE the grade.

• Project submission guidelines updated under the section project

• Project subscription form is online under the section project

• 2022-11-04: No lesson. This time I (Giorgio) caught a flu. Hint: Don't forget to close the window when you sleep ;-)

• 2022-10-31: No lesson. Have a nice long weekend ;-) Lessons restart regularly on 4/11.

• 2022-10-06: The virtual machine is available in the Teaching Material page

• 2022-09-27: Please enroll to the CAD Google classroom to receive updates about the course of studies. HERE

• 2022/09/30: The lessons start on Friday September 30 2022

• All lessons will be in presence. In the lecture page we will link the youtube videos of the corresponding lessons of past years. 

Abstract

This course aims at providing the students with the necessary mathematical background and the practical guidelines to tackle complex problems in state estimation and model identification for robots. At the end of this course the student will possess the necessary background to approach fundamental problems in robotics including, but not limited to localization, simultaneous localization and mapping (SLAM), calibration and tracking. 

Prerequisites

Despite this course in the "manifesto" is scheduled for the first year of the MARR/AIRO studies, it requires reasonably solid notions of  Linear Algebra, Robotics 1 (or their italian versions Geometria and Robotica 1) and Autonomous and Mobile Robotics. For this reason, we recommend the students to sustain the exam during the second year.

Course Enrollment

Please fill the following form to get enrolled in the course.

Enrollment Form

Pics of First Lecture

Exam

ECTS Credits: 6

How to complete the credits for this module: After attending classes, students should: 

• develop a project. 

• sustain a written exam.

Next Exams:

• 2023/01/18, Room A5+A6, via Ariosto, 10:00

• 2023/02/09, Room A5+A6, via Ariosto, 10:00

• 2023/03/28, Room A3, via Ariosto, 17:00

• 2023/06/13, Room 108, Marco Polo, 14:00

• 2023/07/14, Room A5+A6, via Ariosto, 10:00

• 2023/09/01 Room 204, Marco Polo, 14:00

• 12/10/2023, Room A3, Via Ariosto, 13:00

A reminder on what a mark means

• The grade is on the test not on the student: a brilliant student can make a test that scores poorly, and a lucky student can make a test that scores high.

• We evaluate the tests. We do not judge the students.

• Anxiety sucks.

• If you (after looking at the solutions) don't feel your mark is correct we might have misunderstood something (or you wrote hieroglyphs). Come and ask for explanations.

Total grade

• Written test is 3/4 (max mark 30), project takes 1/4 (max mark 8) and the total grade is 32

• Example: 

  • Written part 27/30

  • Project grade 6/8

  • Total grade: 27*24/30 + 6 = 27,6 => 28

Projects 2022/2023

To choose a project please fill the following form.

Project Submission

Prepare a readme file in your repo with some explanation about your project, what did you do and how did you solve it. Embed plots (if required, this depends on the project), some tables with numerical errors and output files required (for this create a folder called `output`) for us to evaluate independently the quality of your work.

Send us an email when you complete the project.

Exam Results

• 18/01/2023

• 09/02/2023

• 28/03/2023

• 14/07/2023

• 01/09/2023

If you wish to discuss the exam, send an email to Luca, Leonardo and Giorgio, with subject [PR-[date_of_exam]-discussion]. We will try to clarify the correction by email, and should this not be sufficient we will schedule an individual meet/zoom/whatsoever.

Preliminary Program

• Probability Primer

  • Axioms of probability 

  • Bayes Rule, Chain Rule, Marginalization and Conditioning 

  • Statistical independence 

• Manipulating pdf representations: marginalizing, conditioning, applying a function

  • Discrete 

  • Gaussian 

  • Particles 

• Dynamic Systems

  • System Model (state, observations, controls)

  • Filtering (Discrete , Kalman and Particle)

• Geometry and Linear Algebra Refresher 

  • vectors, matrices and their operations, SO(X) groups, SE(X) groups 

  • computation of gradients (symbolic, numerical and automatic differentiation) 

• Applications of Filtering

  • Localization (Kalman and Particle)

• SLAM (Kalman and Particle) 

• Least Squares Estimation 

  • Gauss-Newton and Levenberg/Marquardton Euclidean Spaces 

  • Probabilistic Interpretation 

  • Manifolds 

  • Robust Kernels 

• Applications of Least Squares Estimation 

  • Calibration 

  • Sensor Registration (ICP, Bearing-only, Range-Only) 

• Sparse Least Squares 

  • Structure of the problem 

  • Factor Graphs 

  • Sparse Solvers 

• Applications of Sparse Least Squares 

  • Graph-SLAM 

  • Simultaneous Localization, Calibration and Mapping 

• Data Association 

  • Mahalanobis Distance 

  • Voting and Consensus Methods (RANSAC and variants) 

Tentative schedule of the lectures

Week 1: Intro, Sensors, Mobile Platforms, Probability 

Week 2: Manipulating PDF 

Week 3: Dynamic Systems, Filtering, Discrete Filters 

Week 4: Kalman Filters

Week 5: Gaussian Filters

Week 6: Wrapup on Filtering and Applictions: Localization and SLAM

Week 7: Least Squares Estimation 

Week 7: Least Squares Estimation, Applications: Calibration, Sensor Registration 

Week 8: Sparse Least Squares 

Week 9: Applications of Sparse Least Squares: Graph-SLAM 

Week 10: Data Association 

Week 11: Wrapup and Applications

Extras : C++ course by Igor Bogoslavskyi, University of Bonn

Link

We remind that the knowledge of C++ is not mandatory for the exam. Still, we believe that this programming skill might be useful for your career in Robotics.