Schreiber Marcel, M. Sc.

Forschung

Bewegungsschätzung dynamischer Objekte in Gridkarten unter Verwendung von maschinellen Lernverfahren

Lehre

Betreuung des Versuchs "Digitale Regelung" Praktikum Regelungstechnik

Betreuung des Versuchs "Programmierung eines Industrieroboters" Praktikum Mess- und Automatisierungstechnik

Publikationen

M. Schreiber, V. Belagiannis, C. Gläser and K. Dietmayer, "Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks," ArXiv e-prints, Sept. 2019   Preprint available on arXiv

M. Schreiber, S. Hoermann, and K. Dietmayer, "Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks," in 2019 International Conference on Robotics and Automation (ICRA), May 2019, pp. 9299-9305    Available from publisher

Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks

  • Modeling the vehicle environment as dynamic occupancy grid map using recurrent neural networks (RNNs)
  • Occupancy grid maps based on lidar measurements of a single time step serve as input data
  • The recurrent network is trained to predict a dynamic occupancy grid map, i.e. filtered occupancy and velocity of each cell
  • Our approach shows a robust detection of static area, more uniform velocity estimates for dynamic objects and an improved motion estimation in turning maneuvers

 

Long-Term Occupancy Grid Predicition Using Recurrent Neural Networks

  • Prediction of scene evolution based on lidar grid fusion and recurrent neural networks (RNNs)
  • A sequence of dynamic occupancy grid maps is fed to a RNN, which is trained to predict future occupancy grid maps
  • The recurrent network architecture shows the ability to predict occluded objects
  • Spatio-temporal correlations between grid cells are used to predict multimodal future paths and interactions between objects

Funktion

Wissenschaftlicher Mitarbeiter

Kontakt

Schreiber Marcel, M. Sc.
Raum: 41.2.212
Telefon: +49 (0)731 50 27023
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