700.740 (18W) Vision Based State Estimation and Sensor Fusion

Wintersemester 2018/19

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Erster Termin der LV
03.10.2018 11:00 - 12:30 , B04a.1.06
... keine weiteren Termine bekannt

Überblick

Lehrende/r
LV-Titel englisch
Vision Based State Estimation and Sensor Fusion
LV-Art
Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n
2.0
ECTS-Anrechungspunkte
4.0
Anmeldungen
10 (300 max.)
Organisationseinheit
Unterrichtssprache
es wurde keine Unterrichtssprache angegeben
LV-Beginn
03.10.2018
eLearning
zum Moodle-Kurs

LV-Beschreibung

Intendierte Lernergebnisse

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.

Lehrmethodik

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.

Inhalt/e

  • 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

Prüfungsinformationen

Beurteilungsschema

Note/Grade Benotungsschema

Position im Curriculum

  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR) (Wahlfach)
      • 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)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • 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)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR) (Wahlfach)
      • 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)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • 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)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Autonomous Systems and Robotics: Advanced (ASR) (Wahlfach)
      • 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)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Autonomous Systems and Robotics (WI) (Wahlfach)
      • 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)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technischer Schwerpunkt (Control and Measurement Systems) (Pflichtfach)
      • 1.1-1.3 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.740 Vision Based State Estimation and Sensor Fusion (2.0h VC / 4.0 ECTS)

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