3D Semantic Map Computation Based on Depth Map and Video Image
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.
KeywordsBayesian net constraint satisfaction depth map object recognition semantic map
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- 2.Marr, D.: Vision: A computational investigation into the human representation and processing of visual information. New Freeman, New York (1982)Google Scholar
- 3.Russel, S., Norvig, P.: Artificial Intelligence. A modern approach, 2nd edn. Prentice Hall (2002)Google Scholar
- 4.Kasprzak, W., Czajka, Ł., Wilkowski, A.: A Constraint Satisfaction Framework with Bayesian Inference for Model-Based Object Recognition. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part II. LNCS, vol. 6375, pp. 1–8. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 6.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification and Scene Analysis, 2nd edn. J. Wiley, New York (2001)Google Scholar
- 11.Dias, P., Sequeira, V., Vaz, F., Gonalves, J.G.M.: Registration and Fusion of Intensity and Range Data for 3D Modeling of Real World Scenes. In: Proc. 4th International Conference on 3-D Digital Imaging and Modeling, pp. 418–425 (2003)Google Scholar