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Event

Localization of Mobile Agents [SS2224613]

Type
lecture (V)
Präsenz
Term
SS 2022
SWS
3
Language
Deutsch
Appointments
27

Lecturers

Organisation

  • KIT-Fakultät für Informatik

Part of

Literature

Grundlegende Kenntnisse der linearen Algebra und Stochastik sind hilfreich.

Appointments

  • 19.04.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 25.04.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 26.04.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 02.05.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 03.05.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 09.05.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 10.05.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 16.05.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 17.05.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 23.05.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 24.05.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 30.05.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 31.05.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 13.06.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 14.06.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 20.06.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 21.06.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 27.06.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 28.06.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 04.07.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 05.07.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 11.07.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 12.07.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 18.07.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 19.07.2022 14:00 - 15:30 - Room: 50.34 Raum -102
  • 25.07.2022 14:00 - 15:30 - Room: 50.34 Raum -101
  • 26.07.2022 14:00 - 15:30 - Room: 50.34 Raum -102

Note

This module provides a systematic introduction into the topic of localization methods. In order to facilitate understanding, the module is divided into four main topics. Dead reckoning treats the instantaneous determination of a vehicle's position based on dynamic parameters like velocity or steering angle. Localization with the help of measurements of known landmarks is part of static localization. In addition to the closed-form solutions for particular measurements (distances and angles), the least squares method for fusion arbitrary measurements is also introduced. Dynamic localization treats the combination of dead reckoning and static localization. The central part of the lecture is the derivation of the Kalman filter, which has been successfully applied in several practical applications. Finally, simultaneous localization and mapping (SLAM) is introduced, which allows localization in case of (partly) unknown landmark positions.