2021/09/27: Due to bureaucratic issues, the exam session of September will be officially closed on Infostud tomorrow 28th September. All exams that in date 28th September are not concluded due to missing projects will be registered on Infostud in the upcoming exam sessions, when they are completed.
The lessons are planned to start on September 22. Both the lessons and the introduction to practicals will be recorded and made publicly available on some streaming platform.
The lessons will start on October 6, due to delays in setting the rooms for remote teaching.
All lessons will be made available through the following youtube channel (click here).
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.
B121
Monday 11:00, 13:00 + anytime previous email appointment.
email appointment
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 during the second year.
When: Fall semester (about 12 weeks), Start: Tuesday, 2020/09/29
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.
Live on this youtube channel https://www.youtube.com/channel/UCukJt0LRs1cgh_QAq7H0k7g
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:
June 7 2021, 10:00-13:00, Room B2, DIAG
July 7 2021, 10:00-13:00, Room B2, DIAG
September 1 2021, 14:30-17:30, Room B2, DIAG
October 27 2021, 11:00 - 14:00; Room A2, DIAG
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.
If you wish to discuss the exam, send an email to barbara, tiziano and giorgio, with subject [PR-20210211-discussion]. We will try to clarify the correction by email, and should this not be sufficient we will schedule an individual meet/zoom/whatsoever.
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
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.