Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis

PloS One
Benjamin MetkaUte Bauer-Wersing

Abstract

We present a biologically motivated model for visual self-localization which extracts a spatial representation of the environment directly from high dimensional image data by employing a single unsupervised learning rule. The resulting representation encodes the position of the camera as slowly varying features while being invariant to its orientation resembling place cells in a rodent's hippocampus. Using an omnidirectional mirror allows to manipulate the image statistics by adding simulated rotational movement for improved orientation invariance. We apply the model in indoor and outdoor experiments and, for the first time, compare its performance against two state of the art visual SLAM methods. Results of the experiments show that the proposed straightforward model enables a precise self-localization with accuracies in the range of 13-33cm demonstrating its competitiveness to the established SLAM methods in the tested scenarios.

References

Aug 12, 1999·Experimental Brain Research·K J Jeffery, J M O'Keefe
Apr 9, 2002·Neural Computation·Laurenz Wiskott, Terrence J Sejnowski
Aug 9, 2003·Journal of Neurophysiology·Konrad P KördingPeter König
Apr 12, 2006·PLoS Biology·Reto WyssPaul F M J Verschure
Apr 14, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Andrew J DavisonOlivier Stasse
Sep 6, 2007·PLoS Computational Biology·Mathias FranziusLaurenz Wiskott
May 1, 1987·IEEE Transactions on Pattern Analysis and Machine Intelligence·K S ArunS D Blostein
Jul 1, 1991·Neural Computation·Peter Földiák

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Methods Mentioned

BETA
feature extraction

Software Mentioned

RatSLAM
Modular toolkit for Data Processing ( MDP )
ORB
LSD
Slam

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