Abstract
We apply slow feature analysis (SFA) to the problem of self-localization with a mobile robot. A similar unsupervised hierarchical model has earlier been shown to extract a virtual rat’s position as slowly varying features by directly processing the raw, high dimensional views captured during a training run. The learned representations encode the robot’s position, are orientation invariant and similar to cells in a rodent’s hippocampus.
Here, we apply the model to virtual reality data and, for the first time, to data captured by a mobile outdoor robot. We extend the model by using an omnidirectional mirror, which allows to change the perceived image statistics for improved orientation invariance. The resulting representations are used for the notoriously difficult task of outdoor localization with mean absolute localization errors below 6%.
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
Földiak, P.: Learning invariance from transformation sequences. Neural Comput. 3, 194–200 (1991)
Stone, J.V., Bray, A.: A learning rule for extracting spatio-temporal invariances. Network-Comp. Neural 6, 429–436 (1995)
Wiskott, L., Sejnowski, T.: Slow feature analysis: Unsupervised learning of invariances. Neural Comput. 14(4), 715–770 (2002)
Körding, K., Kayser, C., Einhäuser, W., König, P.: How are complex cell properties adapted to the statistics of natural scenes? J. Neurophysiol. 91(1), 206–212 (2004)
Wyss, R., König, P., Verschure, P.: A model of the ventral visual system based on temporal stability and local memory. PLoS Biol. 4, 1–8 (2006)
Berkes, P., Wiskott, L.: Slow feature analysis yields a rich repertoire of complex cell properties. J. Vision 5(6) (2005)
Milford, M., Schulz, R., Prasser, D., Wyeth, G., Wiles, J.: Learning spatial concepts from RatSLAM representations. Robot Auton Syst. 55(5), 403–410 (2007)
Zito, T., Wilbert, N., Wiskott, L., Berkes, P.: Modular toolkit for data processing (mdp): a python data processing framework. Front Neuroinform 2(8) (2009)
Davison, A., Reid, I., Molton, N., Stasse, D.: MonoSLAM: Real-time single camera SLAM. IEEE Trans Pattern Anal. Mach. Int. 29(6), 1052–1067 (2007)
Franzius, M., Sprekeler, H., Wiskott, L.: Slowness and sparseness lead to place, head-direction, and spatial-view cells. PLoS Comp. Biol. 3(8) (2007)
Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)
Smith, A., Balakrishnan, H., Goraczko, M., Priyantha, N.: Tracking Moving Devices with the Cricket Location System. MobiSys (2004)
Jeffery, K., O’Keefe, J.: Learned interaction of visual and idiothetic cues in the control of place field orientation. Exp. Brain Res. 127(2), 151–161 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Metka, B., Franzius, M., Bauer-Wersing, U. (2013). Outdoor Self-Localization of a Mobile Robot Using Slow Feature Analysis. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-42054-2_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42053-5
Online ISBN: 978-3-642-42054-2
eBook Packages: Computer ScienceComputer Science (R0)