Abstract
This paper aims at an approach for labeling places within a grid cell environment. For that we propose a method that is 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. 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). Some Room for GLVQ: Semantic Labeling of Occupancy Grid Maps. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_13
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DOI: https://doi.org/10.1007/978-3-319-07695-9_13
Publisher Name: Springer, Cham
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