Valeria Garro, Giovanni Pintore, Fabio Ganovelli, Enrico Gobbetti, and Roberto Scopigno
October 2016
Abstract
We present a novel algorithm for fast metric reconstruction on mobile devices using a combination of image and inertial acceleration data. In contrast to previous approaches to this problem, our algorithm does not require a long acquisition time or intensive data processing and can be implemented entirely on common IMU-enabled tablet and smartphones. The method recovers real world units by comparing the acceleration values from the inertial sensors with the ones inferred from images. In order to cope with IMU signal noise, we propose a novel RANSAC-like strategy which helps to remove the outliers. We demonstrate the effectiveness and the accuracy of our method through an integrated mobile system returning point clouds in metric scale.
Reference and download information
Valeria Garro, Giovanni Pintore, Fabio Ganovelli, Enrico Gobbetti, and Roberto Scopigno. Fast Metric Acquisition with Mobile Devices. In Proc. 21st International Workshop on Vision, Modeling and Visualization (VMV). Pages 29-36, October 2016. DOI: 10.2312/vmv.20161339.
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Bibtex citation record
@InProceedings{Garro:2016:FMA, author = {Valeria Garro and Giovanni Pintore and Fabio Ganovelli and Enrico Gobbetti and Roberto Scopigno}, title = {Fast Metric Acquisition with Mobile Devices}, booktitle = {Proc. 21st International Workshop on Vision, Modeling and Visualization (VMV)}, pages = {29--36}, month = {October}, year = {2016}, abstract = { We present a novel algorithm for fast metric reconstruction on mobile devices using a combination of image and inertial acceleration data. In contrast to previous approaches to this problem, our algorithm does not require a long acquisition time or intensive data processing and can be implemented entirely on common IMU-enabled tablet and smartphones. The method recovers real world units by comparing the acceleration values from the inertial sensors with the ones inferred from images. In order to cope with IMU signal noise, we propose a novel RANSAC-like strategy which helps to remove the outliers. We demonstrate the effectiveness and the accuracy of our method through an integrated mobile system returning point clouds in metric scale. }, doi = {10.2312/vmv.20161339}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Garro:2016:FMA'}, }
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