Multi-Modal Probabilistic Indoor Localization on a Smartphone

Co-authors

Frederike Dümbgen, Cynthia Oeschger, Mihailo Kolundzija, Emmanuel Girardin, Johan Leuenberger and Serge Ayer.


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Abstract

The satellite-based Global Positioning System (GPS) provides robust localization on smartphones outdoors. In indoor en- vironments, however, no system is close to achieving a similar level of ubiquity, with existing solutions offering different trade-offs in terms of accuracy, robustness and cost.

In this paper, we develop a multi-modal positioning system, targeted at smartphones, which

Embedded polarizing filters to separate diffuse and specular reflection

Co-authors

Laurent Jospin and Gilles Baechler.


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Full text: View at publisher, Arxiv.
Cite: Bibtex.
Code: Will appear shortly.


Abstract

Polarizing filters provide a powerful way to separate diffuse and specular reflection; however, traditional methods rely on several captures and require proper alignment of the filters. Recently, camera manufacturers have proposed to embed polarizing micro-filters in front of the sensor, creating a mosaic of pixels

Combining Range and Direction for Improved Localization

Co-authors

Gilles Baechler, Frederike Dümbgen, Golnoosh Elhami, Miranda Krekovic, Robin Scheibler and Martin Vetterli.


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Full text: Infoscience.
Cite: Bibtex.
Code: GitHub.


Abstract

Self-localization of nodes in a sensor network is typically achieved using either range or direction measurements; in this paper, we show that a constructive combination of both improves the estimation. We propose two localization algorithms that make use of the differences between the sensors’

Shape from bandwidth: The 2-D orthogonal projection case

Co-authors

Benjamín Bejar Haro and Martin Vetterli.


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Full text: View at publisher, Infoscience.
Cite: Bibtex.
Code: Run in browser (using binder), Infoscience.


Abstract

Could bandwidth – one of the most classic concepts in signal processing – have a new purpose? In this paper, we investigate the feasibility of using bandwidth to infer shape from a single image. As a first analysis, we limit our

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