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Confidence-based Cue Integration for Visual Place Recognition
A. Pronobis and B. Caputo
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS07), San Diego, CA, USA, October 2007.
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A distinctive feature of intelligent systems is their capability to analyze their level of
expertise for a given task; in other words, they know what they know. As a way towards this
ambitious goal, this paper presents a recognition algorithm able to measure its own
level of confidence and, in case of uncertainty, to seek for extra information so to increase
its own knowledge and ultimately achieve better performance. We focus on the visual place
recognition problem for topological localization, and we take an SVM approach. We propose a
new method for measuring the confidence level of the classification output, based on the
distance of a test image and the average distance of training vectors. This method is combined
with a discriminative accumulation scheme for cue integration. We show with extensive
experiments that the resulting algorithm achieves better performances for two visual cues than
the classic single cue SVM on the same task, while minimising the computational load.
More important, our method provides a reliable measure of the level of confidence of the decision.
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Created by: Andrzej Pronobis
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Last modified: 20-09-2009 00:26
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