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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mozos, O.M., Triebel, R., Jensfelt, P., Rottmann, A., Burgard, W.: Supervised semantic labeling of places using information extracted from sensor data. RAS 55(5), 391–402 (2007)
Shi, L., Kodagoda, S., Dissanayake, G.: Laser range data based semantic labeling of places. In: IROS, pp. 5941–5946. IEEE (2010)
Shi, L., Kodagoda, S., Dissanayake, G.: Multi-class classification for semantic labeling of places. In: ICARCV, pp. 2307–2312. IEEE (2010)
Sousa, P., Araujo, R., Nunes, U.: Real-Time Labeling of Places using Support Vector Machines. In: ISIE, pp. 2022–2027 (2007)
Nieto-Granda, C., Rogers, J.G., Trevor, A.J., Christensen, H.I.: Semantic map partit. in indoor environments using regional analysis. In: IROS, pp. 1451–1456 (2010)
Bahrmann, F., Hellbach, S., Böhme, H.J.: Please tell me where I am: A fundament for a semantic labeling approach. In: KI, pp. 120–124 (2012)
Pronobis, A., Mozos, O.M., Caputo, B., Jensfelt, P.: Multi-modal semantic place classification. Int J. Robot. Res. 29(2-3), 298–320 (2010)
Koppula, H.S., Anand, A., Joachims, T., Saxena, A.: Semantic labeling of 3d point clouds for indoor scenes. In: NIPS, pp. 244–252 (2011)
Anand, A., Koppula, H.S., Joachims, T., Saxena, A.: Contextually guided semantic labeling and search for three-dimensional point clouds. Int. J. Robot. Res. 32(1), 19–34 (2013)
Hellbach, S., Himstedt, M., Boehme, H.J.: Towards Non-negative Matrix Factorization based Localization. In: ECMR (2013)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. Adv. Neural Inf. Process. Syst 13, 556–562 (2001)
Eggert, J., Wersing, H., Körner, E.: Transformation-invariant representation and NMF. In: IJCNN, pp. 2535–2539 (2004)
Eggert, J., Körner, E.: Sparse Coding and NMF. In: IJCNN, pp. 2529–2533 (2004)
Vollmer, C., Hellbach, S., Eggert, J., Gross, H.M.: Sparse coding of human motion trajectories with non-negative matrix factorization. Neurocomp. (2013)
Paglieroni, D.W.: Distance transforms: properties and machine vision applications. CVGIP: Graph. Models Image Process. 54(1), 56–74 (1992)
Kohonen, T.: The self-organizing map. Proc. of the IEEE 78(9), 1464–1480 (1990)
Sato, A., Yamada, K.: Generalized learning vector quantization. In: NIPS, pp. 423–429. MIT Press, Cambridge (1996)
Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Networks 15(8-9), 1059–1068 (2002)
Schneider, P., Biehl, M., Hammer, B.: Adaptive relevance matrices in learning vector quantization. Neural Computation 21, 3532–3561 (2009)
Qin, A.K., Suganthan, P.N.: A novel kernel prototype-based learning algorithm. In: ICPR (4), pp. 621–624 (2004)
Hammer, B., Strickert, M., Villmann, T.: Supervised neural gas with general similarity measure. Neural Processing Letters 21(1), 21–44 (2005)
Villmann, T., Haase, S.: Divergence-based vector quantization. Neural Computation 23(5), 1343–1392 (2011)
Kästner, M., Riedel, M., Strickert, M., Villmann, T.: Class border sensitive generalized learning vector quantization - an alternative to support vector machines. Machine Learning Reports 6(MLR-04-2012), 40–56 (2012)
Mozos, O.M.: Semantic Place Labeling with Mobile Robots. PhD thesis, Dept. of Computer Science, University of Freiburg (July 2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-07695-9_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07694-2
Online ISBN: 978-3-319-07695-9
eBook Packages: EngineeringEngineering (R0)