Planar Pose Estimation Using Object Detection and Reinforcement Learning

  • Frederik Nørby Rasmussen
  • Sebastian Terp Andersen
  • Bjarne Grossmann
  • Evangelos Boukas
  • Lazaros NalpantidisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


Pose estimation concerns systems or models dealing with the determination of a static object’s pose using, in this case, vision. This paper approaching the problem with an active vision-based solution, that integrates both perception and action in the same model. The problem is solved using a combination of neural networks for object detection and a reinforcement learning architecture for moving a camera and estimating the pose. A robotic implementation of the proposed active vision system is used for testing with promising results. Experiments show that our approach does not only solve the simple task of planar visual pose estimation, but also exhibits robustness to changes in the environment.


Pose estimation Object detection Reinforcement learning 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Frederik Nørby Rasmussen
    • 1
  • Sebastian Terp Andersen
    • 1
  • Bjarne Grossmann
    • 1
  • Evangelos Boukas
    • 2
  • Lazaros Nalpantidis
    • 2
    Email author
  1. 1.Department of Materials and ProductionAalborg UniversityCopenhagenDenmark
  2. 2.Department of Electrical EngineeringTechnical University of DenmarkKongens LyngbyDenmark

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