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Cognitive Systems Engineering C - GET Research Seminar

Welcome to the homepage of the "Cognitive Systems Engineering C - GET Research Seminar" for the summer semester 2023. Here, you will find up-to-date announcements relating to the seminar as well as additional information.

Some of the pages will be updated concurrent with the course. It is worth your while to occasionally check these pages.

Aktuelles

  2023/10/25 Presentations of project group 'Rescue Robots' (part 2)
  2023/10/18 Presentations of project group 'Rescue Robots' (part 1)
  2023/08/30 Master's Thesis Intermediate Presentation: Automated Planning for Behavior Control of Autonomous Rescue Robots
  2023/08/09 Bachelor's Thesis Presentation: Extraktion und Vorverarbeitung von Objektkonturen aus Regionensegmentierungen
  2023/07/10  - 2023/07/12 Presentations of seminar 'Cognitive Systems Engineering'
  2023/07/05 Master's Thesis Intermediate Presentation: Autonomous Navigation in Dynamic Environments Using Multiple Object Tracking Based on Sensor Fusion
  2023/06/28 Bachelor's Thesis Presentation: Analyse aktueller Verfahren zur Langzeit-Objektverfolgung mit einmaliger Initialisierung
  2023/06/14 Master's Thesis Presentation: Real-Time Semantic Aided Place Recognition for Loop Closure Detection
  2023/06/07 Master's Thesis Intermediate Presentation: Unsupervised Deep Learning-based Shape Retrieval Using Invariant Contour Features
  2023/05/24 Master's Thesis Presentation: Surfel based Semantic Maps for Exploration in Dynamic Environments
  2023/05/03 Presentations of project group 'Rescue Robots' (part 2)
  2023/04/26 Master's Thesis Presentation: Robot Arm Control using Hand-Eye Coordination Based on Object Tracking
  2023/04/19 Presentations of project group 'Rescue Robots' (part 1)
  2023/04/12 Master's Thesis Presentation: Spatiotemporal Clustering of Driving Situations Using Unsupervised Learning for Sensor Data Encoding
  2023/04/11 Introductory Meeting, April 11, 13:00 h, P1.6.02.1
  2023/04/05 Master's Thesis Presentation: Deep Learning-based Scale Estimation for Local Image Features