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Inertial Sensor Fusion (ISF)5 ECTS (englische Bezeichnung: Inertial Sensor Fusion)
Modulverantwortliche/r: Thomas Seel Lehrende:
Thomas Seel
Startsemester: |
WS 2022/2023 | Dauer: |
1 Semester | Turnus: |
jährlich (WS) |
Präsenzzeit: |
60 Std. | Eigenstudium: |
90 Std. | Sprache: |
Englisch |
Lehrveranstaltungen:
Empfohlene Voraussetzungen:
Participants should be familiar with fundamentals of linear algebra. It is advantageous but not required to be have some prior knowledge on linear dynamic systems or basic probability theory.
Inhalt:
This module is concerned with inertial sensor technologies and sensor fusion methods for motion tracking of aerial/ground/water vehicles, robotic systems and human body segments. Participants will become familiar with the design and application of methods and algorithms for sensor fusion and analysis of inertial measurement data. This includes methods to estimate the orientation and position of moving objects in three-dimensional space as well as methods for calculating joint angles or segmenting human motion. Since most of the considered applications are feedback-controlled systems, the course focuses on real-time-capable algorithms. The methods will be applied to application data during designated computer exercises that are integrated into the course.
Topics include, but are not limited to:
Basic principles of gyroscopes, accelerometers and magnetometers
Error characteristics of MEMS-based inertial measurement units
Application: Gait phase detection by foot-worn inertial sensors
Quaternions and other representations of 3D rotations
Orientation estimation from inertial measurement data
Application: Position tracking/retrieval of an unmanned aerial vehicle
Joint angle estimation from inertial measurement data
Application: Real-time motion tracking of a robotic actuator
Kalman filtering methods for linear and nonlinear systems
Probabilistic sensor fusion and Bayesian state estimation
Identification of kinematic parameters from inertial measurement data
Application: Human body motion tracking by wearable inertial sensors
Lernziele und Kompetenzen:
- Students are able to employ inertial sensor technologies and sensor fusion methods for applications in research and industry.
They are capable of understanding and handling the complexity of inertial sensor data and have command of a versatile set of methods for real-time processing of inertial measurements.
They are able toi track the orientation and position of an unmanned aerial vehicle.
They are able to track the motion of multi-link kinematic chains, e.g. robotic actuators or human limbs, in three dimensional space.
Literatur:
- Woodman, O.J. An Introduction to Inertial Navigation; University of Cambridge, Computer Laboratory: Cambridge, UK, 2007.
T. Seel, M. Kok, R. McGinnis, "Inertial Sensors—Applications and Challenges in a Nutshell", Sensors 2020, 20, 6221.
M. Kok, J. D. Hol, and T. B. Schön, “An optimization-based approach to human body motion capture using inertial sensors,” IFAC Proceedings Volumes, vol. 47, no. 3, pp. 79–85, Jan. 2014.
B. Taetz, G. Bleser, and M. Miezal, “Towards self-calibrating inertial body motion capture,” in 2016 19th International Conference on Information Fusion (FUSION), Jul. 2016, pp. 1751–1759.
D. Lehmann, D. Laidig, and T. Seel, “Magnetometer-free motion tracking of one-dimensional joints by exploiting kinematic constraints,” Proceedings on Automation in Medical Engineering, vol. 1, no. 1, pp. 027–027, 2020.
D. Laidig, D. Lehmann, M.-A. Bégin, and T. Seel, “Magnetometer-free realtime inertial motion tracking by exploitation of kinematic constraints in 2-dof joints,” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1233–1238, 2019.
M. Caruso, A.M. Sabatini, D. Laidig, T. Seel, M. Knaflitz, U. DellaCroce, A. Cereatti. Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All. Sensors, 21 (7):2543, 2021.
E. A. Wan and R. Van Der Merwe, “The unscented kalman filter for nonlinear estimation,” in Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373), Oct 2000, pp. 153–158.
J. Steinbring and U. D. Hanebeck, “S2kf: The smart sampling kalman filter,” in Proceedings of the 16th International Conference on Information Fusion, 2013, pp. 2089–2096.
A. Solin, S. Särkkä, J. Kannala, and E. Rahtu, “Terrain navigation in the magnetic landscape: Particle filtering for indoor positioning,” 05 2016, pp. 1–9.
Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan: Das Modul ist im Kontext der folgenden Studienfächer/Vertiefungsrichtungen verwendbar:
- Elektrotechnik, Elektronik und Informationstechnik (Bachelor of Science)
(Po-Vers. 2019w | TechFak | Elektrotechnik, Elektronik und Informationstechnik (Bachelor of Science) | Gesamtkonto | Wahlfächer | Technische Wahlfächer (aus dem Angebot der Technischen Fakultät frei wählbar) | Inertial Sensor Fusion)
Studien-/Prüfungsleistungen:
Inertial Sensor Fusion (Prüfungsnummer: 23581)
(englischer Titel: Written Exam ISF)
- Prüfungsleistung, Klausur mit MultipleChoice, Dauer (in Minuten): 60, benotet, 5 ECTS
- Anteil an der Berechnung der Modulnote: 100.0 %
- weitere Erläuterungen:
Answering the questions requires understanding of the concepts taught throughout the course and the ability to apply these concepts to specific example problems. The exam contains multiple-choice questions. It counts 100% of the course grade. By submitting small homework assignments, up to 20% of bonus points can be obtained, which will be added to the result of the exam.
- Prüfungssprache: Englisch
- Erstablegung: WS 2022/2023, 1. Wdh.: SS 2023
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