700.740 (20W) Vision Based State Estimation and Sensor Fusion
Overview
For further information regarding teaching on campus, please visit: https://www.aau.at/en/corona.
- Lecturer
- Course title german Vision Based State Estimation and Sensor Fusion
- Type Lecture - Course (continuous assessment course )
- Course model Online course
- Hours per Week 2.0
- ECTS credits 4.0
- Registrations 8 (300 max.)
- Organisational unit
- Language of instruction Englisch
- Course begins on 07.10.2020
- eLearning Go to Moodle course
Time and place
Course Information
Intended learning outcomes
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.
Teaching methodology including the use of 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.
Course content
- 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
Examination information
Examination methodology
Final written exam
(open book)
Examination topic(s)
Content of the lecture
Grading scheme
Grade / Grade grading schemePosition in the curriculum
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Information and Communications Engineering: Supplements (NC, ASR)
(Compulsory elective)
-
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)
-
Subject: Information and Communications Engineering: Supplements (NC, ASR)
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
-
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)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Information and Communications Engineering: Supplements (NC, ASR)
(Compulsory elective)
-
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)
-
Subject: Information and Communications Engineering: Supplements (NC, ASR)
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
-
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)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Autonomous Systems and Robotics: Advanced (ASR)
(Compulsory elective)
-
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)
-
Subject: Autonomous Systems and Robotics: Advanced (ASR)
(Compulsory elective)
- Master's degree programme Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Subject: Autonomous Systems and Robotics (WI)
(Compulsory elective)
-
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)
-
Subject: Autonomous Systems and Robotics (WI)
(Compulsory elective)
Equivalent courses for counting the examination attempts
-
Wintersemester 2024/25
- 700.740 VC Vision Based State Estimation and Sensor Fusion (2.0h / 4.0ECTS)
-
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 2019/20
- 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)