700.741 (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 Course (continuous assessment course )
- Course model Online course
- Hours per Week 2.0
- ECTS credits 3.0
- Registrations 4 (20 max.)
- Organisational unit
- Language of instruction English
- 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
Theory from the lecture "Vision Based State Estimation and Sensor Fusion" (700.740) will be deepened in this practical course with exercises (KU 700.741 ).
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
Exam Preparation
Examination information
Examination methodology
Exercise sheets to solve and submit every week with a discussion of the solutions during the course (Classroom).
Examination topic(s)
See lecture overview.
Assessment criteria / Standards of assessment for examinations
The percentage of solved tasks to given tasks will give you the grade. The performance during the presentation of the solution influences the grading as well.
(Details in Moodle course)
Grading scheme
Grade / Grade grading schemePosition in the curriculum
- Masterstudium 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)
- 700.741 Vision Based State Estimation and Sensor Fusion (2.0h KS / 3.0 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)
- Masterstudium 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)
- 700.741 Vision Based State Estimation and Sensor Fusion (2.0h KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
- Masterstudium 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)
- 700.741 Vision Based State Estimation and Sensor Fusion (2.0h KS / 3.0 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)
- Masterstudium 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)
- 700.741 Vision Based State Estimation and Sensor Fusion (2.0h KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
- Masterstudium 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)
- 700.741 Vision Based State Estimation and Sensor Fusion (2.0h KS / 3.0 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)
- Masterstudium 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)
- 700.741 Vision Based State Estimation and Sensor Fusion (2.0h KS / 3.0 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 2022/23
- 700.741 KS Vision Based State Estimation and Sensor Fusion (2.0h / 3.0ECTS)
-
Wintersemester 2021/22
- 700.741 KS Vision Based State Estimation and Sensor Fusion (2.0h / 3.0ECTS)
-
Wintersemester 2019/20
- 700.741 KS Vision Based State Estimation and Sensor Fusion (2.0h / 3.0ECTS)
-
Wintersemester 2018/19
- 700.741 KS Vision Based State Estimation and Sensor Fusion (2.0h / 3.0ECTS)
-
Wintersemester 2017/18
- 700.741 KS Vision Based State Estimation and Sensor Fusion (2.0h / 3.0ECTS)
-
Wintersemester 2016/17
- 700.741 KS Vision Based State Estimation and Sensor Fusion (2.0h / 3.0ECTS)