3D Semantic Map Computation Based on Depth Map and Video Image

  • Włodzimierz Kasprzak
  • Maciej Stefańczyk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)


A model-based object recognition in video and depth images is proposed for the purpose of semantic map creation in mobile robotics. Three types of objects are modeled: a human silhouette, a chair/table and corridor walls. A bi-driven hypothesis generation and verification strategy is outlined. The object model includes a hierarchic semantic nets, combined with a graph of constraints and a Bayesian network for hypothesis generation and evaluation. For the purpose of model-to-image matching we define an incomplete constraint satisfaction problem and solve it. Our CSP-search allows partial assignment solutions and uses a stochastic inference to provide judgments of such solutions. The verification of hypotheses is due to a top-down occlusion propagation process, that explains why some object parts are hidden or occluded.


Bayesian net constraint satisfaction depth map object recognition semantic map 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Włodzimierz Kasprzak
    • 1
  • Maciej Stefańczyk
    • 2
  1. 1.Industrial Research Institute for Automation and MeasurementsWarszawaPoland
  2. 2.Institute of Control and Computation EngineeringWarsaw University of TechnologyWarsawPoland

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