700.740 (24W) Vision Based State Estimation and Sensor Fusion

Wintersemester 2024/25

Registration possible from
30.08.2024 00:00

First course session
02.10.2024 11:45 - 13:15 B04.1.06 On Campus
Next session:
09.10.2024 11:45 - 13:15 B04.1.06 On Campus

Overview

Lecturer
Course title german Vision Based State Estimation and Sensor Fusion
Type Lecture - Course (continuous assessment course )
Course model Attendance-based course
Hours per Week 2.0
ECTS credits 4.0
Registrations 0 (300 max.)
Organisational unit
Language of instruction Englisch
Course begins on 02.10.2024

Time and place

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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

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

Prior knowledge expected

 

Curricular registration requirements

 

Literature

 

Link to further information

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

Assessment criteria / Standards of assessment for examinations

 

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)
  • Master's degree programme Information and Communications Engineering (ICE) (SKZ: 488, Version: 22W.1)
    • Subject: Autonomous Systems and Robotics: Advanced (Compulsory elective)
      • 2.2 Vision Based State Estimation and Sensor Fusion ( 0.0h VC / 4.0 ECTS)
        • 700.740 Vision Based State Estimation and Sensor Fusion (2.0h VC / 4.0 ECTS)

Equivalent courses for counting the examination attempts

Wintersemester 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 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)