Event
Localization of Mobile Agents [SS2424613]
Type
lecture (V)Präsenz
Term
SS 2024SWS
3Language
DeutschAppointments
23Organisation
- KIT-Fakultät für Informatik
Part of
Literature
Grundlegende Kenntnisse der linearen Algebra und Stochastik sind hilfreich.
Appointments
- 16.04.2024 14:00 - 15:30 - Room: 50.34 Raum -102
- 22.04.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 23.04.2024 14:00 - 15:30 - Room: 50.34 Raum -102
- 29.04.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 30.04.2024 14:00 - 15:30 - Room: 50.34 Raum -102
- 06.05.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 07.05.2024 14:00 - 15:30 - Room: 50.34 Raum -102
- 13.05.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 14.05.2024 14:00 - 15:30 - Room: 50.34 Raum -102
- 27.05.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 28.05.2024 14:00 - 15:30 - Room: 50.34 Raum -102
- 03.06.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 04.06.2024 14:00 - 15:30 - Room: 50.34 Raum -102
- 10.06.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 11.06.2024 14:00 - 15:30 - Room: 50.34 Raum -102
- 17.06.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 18.06.2024 14:00 - 15:30 - Room: 50.34 Raum -102
- 24.06.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 01.07.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 02.07.2024 14:00 - 15:30 - Room: 50.34 Raum -102
- 08.07.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 15.07.2024 14:00 - 15:30 - Room: 50.34 Raum -101
- 22.07.2024 14:00 - 15:30 - Room: 50.34 Raum -101
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.