Abstract
Semi-automatic semantic labeling of occupancy grid maps has numerous applications for assistance robotic. This paper proposes an approach based on non-negative matrix factorization (NMF) to extract environment specific features from a given occupancy grid map. NMF also computes a description about where on the map these features need to be applied. We use this description after certain pre-processing steps as an input for generalized learning vector quantization (GLVQ) to achieve the classification or labeling of the grid cells. For the supervised training of the GLVQ the assigned label is propagated to all grid cells of a semantic unit using a simple, yet effective segmentation algorithm. Our approach is evaluated on a standard data set from University of Freiburg, showing very promising results.
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Hellbach, S., Himstedt, M., Bahrmann, F., Riedel, M., Villmann, T., Böhme, HJ. (2014). Find Rooms for Improvement: Towards Semi-automatic Labeling of Occupancy Grid Maps. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_66
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DOI: https://doi.org/10.1007/978-3-319-12643-2_66
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