700.741 (20W) Vision Based State Estimation and Sensor Fusion

Wintersemester 2020/21

Registration deadline has expired.

First course session
07.10.2020 16:30 - 18:00 online (Moodle/Classroom) 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 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

Please note that the currently displayed dates may be subject to change due to COVID-19 measures.
List of events is loading...

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

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

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 scheme

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

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)