700.740 (19W) Vision Based State Estimation and Sensor Fusion
Überblick
- Lehrende/r
- LV-Titel englisch Vision Based State Estimation and Sensor Fusion
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 6 (300 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 09.10.2019
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
In this lecture, the student will acquire knowledge on how to model sensors with their uncertainties and how to use these models for probabilistic state estimation through non-linear estimators and non-linear optimization methods. In particular, the student will learn how to model a camera and use it as a sensor to track the pose in 6DoF. Furthermore, the lecture will discuss the non-linear observability analysis of said estimators.
Lehrmethodik inkl. Einsatz von eLearning-Tools
This lecture is closely linked to its practical counterpart (i.e. exercises) KU 700.741 "Vision Based State Estimation and Sensor Fusion". Students are highly encouraged to enroll in that course since the theoretical aspects in 700.740 (this lecture) will be deepened in the practical course.
Inhalt/e
- Overview:
- Sensor Modeling Overview
- Camera Overview
- Camera I:
- Distortion
- Epipolar Geometry
- Camera II:
- Camera Matrix
- Camera Calibration
- Camera III:
- Features
- Pose Estimation
- Camera IV:
- N-Point Algorithm
- Ransac
- Sensors:
- Barometer Model
- GPS Model
- IMU Model
- Continous/Discrete
- Filter I:
- State Estimation Overview
- Filter Overview
- Maximum Likelihood
- Wiener Filter
- Filter II:
- Kalman Filter
- Extended Kalman Filter
- Filter III:
- Unscented Kalman Filter
- Iterative Extended Kalman Filter
- Sensor Fusion Overview
- Simplification of H
- Bundle Adjustment:
- Bundle Adjustment
- Matirx sparsety/FactorGraphs (Simplification of H)
- IMU-preintegration
- Observability Analysis:
- Observability Analysis
- Linear OA
- Nonlinear OA
- SLAM/VIO
- Case study
- Exam Preparation
Prüfungsinformationen
Prüfungsmethode/n
Final written exam
(open book)
Prüfungsinhalt/e
Content of the lecture
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.740 Vision Based State Estimation and Sensor Fusion (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.740 Vision Based State Estimation and Sensor Fusion (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.740 Vision Based State Estimation and Sensor Fusion (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.740 Vision Based State Estimation and Sensor Fusion (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
- 700.740 Vision Based State Estimation and Sensor Fusion (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Autonomous Systems and Robotics (WI)
(Wahlfach)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
- 700.740 Vision Based State Estimation and Sensor Fusion (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Fach: Autonomous Systems and Robotics (WI)
(Wahlfach)
- Masterstudium Information Technology
(SKZ: 489, Version: 06W.3)
-
Fach: Technischer Schwerpunkt (Control and Measurement Systems)
(Pflichtfach)
-
1.1-1.3 Vorlesung mit Kurs oder Vorlesung mit Seminar (
6.0h VK/VS / 12.0 ECTS)
- 700.740 Vision Based State Estimation and Sensor Fusion (2.0h VC / 4.0 ECTS)
-
1.1-1.3 Vorlesung mit Kurs oder Vorlesung mit Seminar (
6.0h VK/VS / 12.0 ECTS)
-
Fach: Technischer Schwerpunkt (Control and Measurement Systems)
(Pflichtfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Wintersemester 2022/23
- 700.740 VC Vision Based State Estimation and Sensor Fusion (2.0h / 4.0ECTS)
-
Wintersemester 2021/22
- 700.740 VC Vision Based State Estimation and Sensor Fusion (2.0h / 4.0ECTS)
-
Wintersemester 2020/21
- 700.740 VC Vision Based State Estimation and Sensor Fusion (2.0h / 4.0ECTS)
-
Wintersemester 2018/19
- 700.740 VC Vision Based State Estimation and Sensor Fusion (2.0h / 4.0ECTS)
-
Wintersemester 2017/18
- 700.740 VC Vision Based State Estimation and Sensor Fusion (2.0h / 4.0ECTS)
-
Wintersemester 2016/17
- 700.740 VC Vision Based State Estimation and Sensor Fusion (2.0h / 4.0ECTS)