Moses - State Estimation for Robotics

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Module / Version:
#41018 / #1

Validity:
Seit WiSe 2020/21

Default display language:
English

Module title:
State Estimation for Robotics

Credits:
6

Responsible person:
Gallego, Guillermo

Grading:
benotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Technische Informatik und Mikroelektronik

Area of expertise:
34342000 FG Robotic Interactive Perception

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 5-5

Contact person:
No information

E-mail address:
guillermo.gallego@tu-berlin.de

Website:

https://www.digital-future.berlin/en/about-us/professors/prof-dr-guillermo-gallego/

Learning Outcomes

Participants will learn theoretical foundations and relevant algorithms developed in the field of state estimation for robotics. State estimation is key for determining unknown variables in dynamical systems. In robotics it is paramount to determine the state of a robot (a set of quantities, such as position, orientation, and velocity) because once known it fully describes that robot’s motion over time. It is often closely identified with Bayesian filtering and its application to SLAM (Simultaneous Localization and Mapping). Upon completing the module, participants will have an overview of the field of state estimation and its toolbox. Students will be able to understand robotics systems on SLAM, ego-motion / attitude estimation and sensor fusion. They will be able to identify key performance metrics, applications, advantages and disadvantages of the methods in order to pick the best tool for the job.

Content

This course follows the book “State Estimation for Robotics” by Timothy Barfoot, Cambridge University Press (available online, follow the link below for a table of contents). Like the book, the course has a considerable mathematical payload. The topics covered include the following: optimization, least squares, linear-Gaussian estimation, nonlinear non-Gaussian estimation, uncertainty propagation, Bayesian filtering, Kalman filtering (KF), extended KF, particle filters, recursive estimation, state parameterizations, Simultaneous Localization and Mapping (SLAM) and continuous-time estimation. The course will provide details on how to tailor general estimation results to robots operating in three-dimensional space, advocating the matrix Lie group approach to handling rotations and poses. For the interested reader, a one-page summary of the history of estimation is provided in Section 1.1 of Barfoot’s book.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
State Estimation for Robotics	VL	3434 L 10774	WiSe	English	6

Workload and Credit Points

State Estimation for Robotics (VL):

Workload description	Multiplier	Hours	Total
Attendance	15.0	6.0h	90.0h
Pre/post processing	15.0	6.0h	90.0h
			180.0h(~6 LP)

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

The lectures will present the topics (concepts and algorithms) from a theoretical point of view, highlighting the underlying principles and mathematical tools used. Participation/interaction is encouraged and expected, including the possibility of reading assignments. Participants are expected to rehearse topics after class in preparation for the exercises. The exercises are both theoretical and practical. The theoretical ones consist of mathematical problem solving to deepen the knowledge about the methods. The practical ones consist of partial implementation (in software) of the algorithms presented. These offer the participants the opportunity to get practical insights about the technology of state estimation.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Curiosity to learn something new and from a solid mathematical point of view. Knowledge of linear algebra, calculus, probability theory and computer programming is strongly recommended. Knowledge in robotics and computer vision is beneficial.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

Name	Points	Categorie	Duration/Extent
(Deliverable assessment) Final program code	30	practical	4-6 pages
(Examination) Written exam	40	written	80 minutes
(Deliverable assessment) Program code	30	practical	2-4 pages

Grading scale

Notenschlüssel »Notenschlüssel 1: Fak IV (1)«

Gesamtpunktzahl	1.0	1.3	1.7	2.0	2.3	2.7	3.0	3.3	3.7	4.0
100.0pt	86.0pt	82.0pt	78.0pt	74.0pt	70.0pt	66.0pt	62.0pt	58.0pt	54.0pt	50.0pt

Test description (Module completion)

No information

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Wintersemester.

Maximum Number of Participants

The maximum capacity of students is 30.

Registration Procedures

For any questions about the module, contact Prof. Gallego guillermo.gallego@tu-berlin.de

Recommended literature
“State Estimation for Robotics”, by Timothy Barfoot, Cambridge University Press. Available online: http://asrl.utias.utoronto.ca/~tdb/bib/barfoot_ser17.pdf
“Robotics, Vision and Control”, by Peter Corke, 2nd Ed, Springer Tracts in Advanced Robotics.

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
Computational Engineering Science (Informationstechnik im Maschinenwesen) (M. Sc.)	1	1	SoSe 2024	SoSe 2024
Computer Engineering (M. Sc.)	1	32	WiSe 2020/21	SoSe 2024
Computer Science (Informatik) (M. Sc.)	1	16	WiSe 2020/21	SoSe 2024
Elektrotechnik (M. Sc.)	1	24	WiSe 2020/21	SoSe 2024

Miscellaneous