2020-10-25: The next exam whose official date is 2020-10-26 will be done remotely, due to the worsening COVID situation. Send an email to Tiziano and Giorgio to be scheduled. The exams will take place on the Wednesdays of the upcoming weeks.
2020-05-18: Due to bureaucracy issues, we have to close the exam reports for all the sessions. For those of you that have completed the written part, but still have not delivered the project is not necessary to book for another session. When you complete the project, just send an email to Tiziano and Prof. Grisetti and the exam will be verbalized automatically by them.
2020-04-28: The Faculty has published the OPIS code for this course: XRDU8U3E. Use it when you fill the form on infostud.
2020-01-20: Amended Rules for the Exam. The project is worth up to 3 points.
2019-10-28: Next Friday lectures will take place in Room A4, instead of Room A5
2019-10-18: Today's lecture MOVED to Room A7
2019-10-01: EXERCISE - bring a laptop and clone the repo today (2019-09-30), after 19:00.
2019-09-30: updated slides on dynamic Bayesian nets (04).
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.
Monday 11:00, 13:00 + anytime previous email appointment.
gone :(
going :(
email appointment
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 on the second year.
When: Fall semester (about 12 weeks), Start: Tuesday, 2019/09/24
Tuesday 10:00-13:00, Room A5
Friday 14:00-16:00, Room A4
Where: Dipartimento di Ingegneria informatica automatica e gestionale Antonio Ruberti (DIAG) , Sapienza University of Rome.
Via Ariosto 25, I-00185, Rome, Italy.
ECTS Credits: 6
How to complete the credits for this module: After attending classes, students should:
develop a small project.
sustain a written exam.
Next Exams:
Date: 08/01/2020, Time: 9.00 to 13.00, Place: Room B2, Via Ariosto 25 (reserved for erasmus and students enrolled before 2018/2019 )
Date: 17/1/2020, Time: 9.00 to 13.00, Place: Rooms A4/A5, Via Ariosto 25 (students that partecipate to the 08/01 cannot attend this exam)
Date: 29/01/2020, Time: 9.00 to 13.00, Place: Room B2, Via Ariosto 25 (moved forward - see line below)
Date: 07/02/2020, Time: 9.00 to 13.00, Place: Room B2, Via Ariosto 25
Date: 10/02/2020, Time: 14.00 to 16.00, Place: Room A7, Via Ariosto 25 (reserved for erasmus and students enrolled before 2018/2019. Those who attend to the 07/02 cannot attend this exam )
Date: 26/03/2020, Time: 14.00 to 18.00, Place : Room A7, Via Ariosto 25
Date: 07/04/2020, Time: 11:00 to 13.00, Place : Room B2 Via Ariosto 25
Date: 12/05/2020, Time: 14:00 to 19.00, Place: Google Meet (Access with your institutional account)
Date: 10/06/2020, Time: 10.00 to 13.00, Place: Room B2, Via Ariosto 25
Date: 08/07/2020, Time: 10.00 to 13.00, Place: Room B2, Via Ariosto 25
Date: 02/09/2020, Time: 9.00 to 13.00, Place: Room B2, Via Ariosto 25
Given the current situation of the COVID-19, it is possible to take the exam in oral form if you can prove the impossibility of being present in the classroom during the exam. The date will be established after the written exam. The two modalities are mutually exclusive.
Date: 26/10/2020, Time: TBD, Place: TBD
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.
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)
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