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Vision-based robot localization based on the efficient matching of planar features.

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Title: Vision-based robot localization based on the efficient matching of planar features.
Authors: Charmette, Baptiste baptiste.charmette@univ-bpclermont.fr, Royer, Eric eric.royer@univ-bpclermont.fr, Chausse, Frédéric frederic.chausse@univ-bpclermont.fr
Source: Machine Vision & Applications. May2016, Vol. 27 Issue 4, p415-436. 22p.
Subjects: Matching Familiar Figures Test, Descriptor systems, Localization problems (Robotics), Three-dimensional modeling, Image reconstruction
Abstract: Real-time accurate localization is a key component of any autonomous mobile robot. Visual localization algorithms usually rely on feature matching between the current view and a map using point descriptors. Many descriptors such as SIFT or SURF are designed to recognize features seen from different viewpoints, but in robotics context, robot movement can be modeled to bring useful information for the matching problem. Here we detail a feature-matching solution using a local 3D model of the features that exploits the motion model of the robot. We compare our method against the SIFT descriptor in a simple matching experiment. The method is then combined with prediction models to achieve autonomous navigation of a mobile robot. Experiments showed that localization remains possible despite severe viewpoint change. [ABSTRACT FROM AUTHOR]
Copyright of Machine Vision & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Charmette%2C+Baptiste%22">Charmette, Baptiste</searchLink><i> baptiste.charmette@univ-bpclermont.fr</i><br /><searchLink fieldCode="AR" term="%22Royer%2C+Eric%22">Royer, Eric</searchLink><i> eric.royer@univ-bpclermont.fr</i><br /><searchLink fieldCode="AR" term="%22Chausse%2C+Frédéric%22">Chausse, Frédéric</searchLink><i> frederic.chausse@univ-bpclermont.fr</i>
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  Data: Real-time accurate localization is a key component of any autonomous mobile robot. Visual localization algorithms usually rely on feature matching between the current view and a map using point descriptors. Many descriptors such as SIFT or SURF are designed to recognize features seen from different viewpoints, but in robotics context, robot movement can be modeled to bring useful information for the matching problem. Here we detail a feature-matching solution using a local 3D model of the features that exploits the motion model of the robot. We compare our method against the SIFT descriptor in a simple matching experiment. The method is then combined with prediction models to achieve autonomous navigation of a mobile robot. Experiments showed that localization remains possible despite severe viewpoint change. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Machine Vision & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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