700.740 (20W) Vision Based State Estimation and Sensor Fusion

Wintersemester 2020/21

Registration deadline has expired.

First course session
07.10.2020 11:00 - 12:30 Online Off Campus
... no further dates known

Overview

Due to the COVID-19 pandemic, it may be necessary to make changes to courses and examinations at short notice (e.g. cancellation of attendance-based courses and switching to online examinations).

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

Please note that the currently displayed dates may be subject to change due to COVID-19 measures.
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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

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Examination methodology

Final written exam
(open book)

Examination topic(s)

Content of the lecture

Grading scheme

Grade / Grade grading scheme

Position 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)

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)