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. © Springer International Publishing Switzerland 2014.
CITATION STYLE
Hellbach, S., Himstedt, M., Bahrmann, F., Riedel, M., Villmann, T., & Böhme, H. J. (2014). Some Room for GLVQ: Semantic Labeling of Occupancy Grid Maps. In Advances in Intelligent Systems and Computing (Vol. 295, pp. 133–143). Springer Verlag. https://doi.org/10.1007/978-3-319-07695-9_13
Mendeley helps you to discover research relevant for your work.