E-mail: niklas.stannartz@tu-dortmund.de

Research focus

• Efficient landmark-based localization in HD maps using semantic sensor information
• Recognition of outdated HD map data by the vehicle's own sensor technology

Conference contributions and presentations

2021

• Stannartz, N., L. Jui-Lin, M. Waldner, T. Bertram: Semantic Landmark-based HD Map Localization Using Sliding Window Max-Mixture Factor Graphs, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021
  25.10.2021

2020

• Schmidt, M., N. Stannartz and T. Bertram: Analysis of Depth Estimation and Semantic Segmentation Algorithms for the Environment Perception of Automated Vehicles, ATZ live - Automatisiertes Fahren 2020, Wiesbaden, October 2020
• Stannartz, N., M. Theers, A. Llarena, M. Sons, M. Kuhn and T. Bertram: Comparison of Curve Representations for Memory-Efficient and High-Precision Map Generation, ^23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Virtual Conference, September 2020
• Bartsch, K., N. Stannartz, M. Schmidt, T. Bertram: Functional Simulation of automotive Lidar and Camera Sensors, presented at AmE 2020 - Automotive meets Electronics; ^11th GMM-Symposium
  March 2020.

2019

• Oeljeklaus, M., N. Stannartz, M. Schmidt, F. Hoffmann and T. Bertram: Vehicle detection with stationary cameras for automatic traffic monitoring, AUTOREG 2019, Mannheim, pp. 67-76
  July 2019
• Stannartz, N., C. Wissing, M. Krüger, A. Tolmidis, S. Ali, T. Nattermann and T. Bertram: Ego-Motion Correction based on Static Objects detected by an Automotive Lidar Sensor System, Automotive meets Electronics (AmE), Dortmund
  March 2019
• Stannartz, N., A. Tolmidis, O. Kind and T. Bertram: Comparison of graph- and Kalman-filter-based methods for improved ego-motion estimation based on detected static objects, International Federation for the Promotion of Mechanism and Machine Science (IFToMM D-A-CH), Aachen
  February 2019

Journal and book contributions

2021

• Diehl,C., N.Stannartz, T. Bertram : Navigation with Uncertain Map Data for Automated Vehicles
  Springer Fachmedien Wiesbaden
• Stannartz, N., M. Theers, M. Sons; A. Llarena, M. Kuhn, O.M. Kind, T. Bertram : Efficient Localization on Highways Employing Public HD Maps and Series-Production Sensors, 21st International Stuttgart Symposium. Springer Vieweg, Wiesbaden, 2021
  14.05.2021

2019

• Oeljeklaus, M., N. Stannartz, M. Schmidt, F. Hoffmann and T. Bertram: Fahrzeugdetektion mit stationären Kameras zur automatischen Verkehrsüberwachung, Forschung im Ingenieurwesen 2019, Springer, Berlin
  May 2019

Student research projects / Bachelor's / Master's theses

• L. Meier-Ebert:
  Analysis of depth estimation algorithms for the environment perception of automated vehicles,
  Master's thesis at the Chair of Control Systems Engineering at Dortmund University of Technology,
  November 2019.
  Supervision: M. Schmidt, N. Stannartz, T. Bertram
• J. Adamek:
  Development of a model for deriving lane change requests of automated vehicles,
  Bachelor's thesis at the Chair of Control Systems Engineering at Dortmund University of Technology,
  September 2019.
  Supervision: M. Schmidt, N. Stannartz, T. Bertram
• M. Nikolov:
  Comparison of 3D-Lidar-SLAM Methods for high-precision Mapping and Localization,
  Bachelorthesis am Lehrstuhl für Regelungssystemtechnik der Technischen Universität Dortmund,
  August 2019.
  Supervision: N. Stannartz, M. Schmidt, T. Bertram
• K. Bartsch:
  Echtzeitfähige Objektdetektion von exterozeptiven Fahrzeugsensoren zur Simulation und Optimierung verschiedener Sensortopologien,
  Masterthesis am Lehrstuhl für Regelungssystemtechnik der Technischen Universität Dortmund,
  May 2019.
  Supervision: N. Stannartz, M. Schmidt, T. Bertram