Event based self-supervised temporal integration for multimodal sensor data.

Emilia I Barakova; Tino Lourens

doi:10.1142/s021963520500077x

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Event based self-supervised temporal integration for multimodal sensor data.

Emilia I Barakova·Tino Lourens

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Abstract

A method for synergistic integration of multimodal sensor data is proposed in this paper. This method is based on two aspects of the integration process: (1) achieving synergistic integration of two or more sensory modalities, and (2) fusing the various information streams at particular moments during processing. Inspired by psychophysical experiments, we propose a self-supervised learning method for achieving synergy with combined representations. Evidence from temporal registration and binding experiments indicates that different cues are processed individually at specific time intervals. Therefore, an event-based temporal co-occurrence principle is proposed for the integration process. This integration method was applied to a mobile robot exploring unfamiliar environments. Simulations showed that integration enhanced route recognition with many perceptual similarities; moreover, they indicate that a perceptual hierarchy of knowledge about instant movement contributes significantly to short-term navigation, but that visual perceptions have bigger impact over longer intervals.

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Animals Electronic Data Processing Models, Neurological Neural Networks, Computer Perception Recognition, Psychology Sensation Transducers Journal Article

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Emilia I Barakova, Tino Lourens. 2005. Event based self-supervised temporal integration for multimodal sensor data.. https://doi.org/10.1142/s021963520500077x

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