Abstract
In This paper, we introduce a new covariance based feature descriptor To be used on 'colored' point clouds gathered by a mobile robot equipped with an RGB-D camera. Although many recent descriptors provide adequate results, There is not yet a clear consensus on how To best Tackle 'colored' point clouds. We present The notion of a covariance on RGB-D data. Covariances have not only been proven To be successful in image processing, but in other domains as well. Their main advantage is That They provide a compact and flexible description of point clouds. Our work is a first step Towards demonstrating The usability of The concept of covariances in conjunction with RGB-D data. Experiments performed on an RGB-D database and compared To previous results show The increased performance of our method.
| Original language | English (US) |
|---|---|
| Article number | 6907663 |
| Pages (from-to) | 5467-5472 |
| Number of pages | 6 |
| Journal | Proceedings - IEEE International Conference on Robotics and Automation |
| DOIs | |
| State | Published - Sep 22 2014 |
| Event | 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China Duration: May 31 2014 → Jun 7 2014 |
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RGB-D object classification using covariance descriptors. / Fehr, Duc; Beksi, William J.; Zermas, Dimitris; Papanikolopoulos, Nikolaos.
In: Proceedings - IEEE International Conference on Robotics and Automation, 22.09.2014, p. 5467-5472.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - RGB-D object classification using covariance descriptors
AU - Fehr, Duc
AU - Beksi, William J.
AU - Zermas, Dimitris
AU - Papanikolopoulos, Nikolaos
PY - 2014/9/22
Y1 - 2014/9/22
N2 - In This paper, we introduce a new covariance based feature descriptor To be used on 'colored' point clouds gathered by a mobile robot equipped with an RGB-D camera. Although many recent descriptors provide adequate results, There is not yet a clear consensus on how To best Tackle 'colored' point clouds. We present The notion of a covariance on RGB-D data. Covariances have not only been proven To be successful in image processing, but in other domains as well. Their main advantage is That They provide a compact and flexible description of point clouds. Our work is a first step Towards demonstrating The usability of The concept of covariances in conjunction with RGB-D data. Experiments performed on an RGB-D database and compared To previous results show The increased performance of our method.
AB - In This paper, we introduce a new covariance based feature descriptor To be used on 'colored' point clouds gathered by a mobile robot equipped with an RGB-D camera. Although many recent descriptors provide adequate results, There is not yet a clear consensus on how To best Tackle 'colored' point clouds. We present The notion of a covariance on RGB-D data. Covariances have not only been proven To be successful in image processing, but in other domains as well. Their main advantage is That They provide a compact and flexible description of point clouds. Our work is a first step Towards demonstrating The usability of The concept of covariances in conjunction with RGB-D data. Experiments performed on an RGB-D database and compared To previous results show The increased performance of our method.
UR - http://www.scopus.com/inward/record.url?scp=84929208210&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929208210&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2014.6907663
DO - 10.1109/ICRA.2014.6907663
M3 - Conference article
AN - SCOPUS:84929208210
SP - 5467
EP - 5472
JO - Proceedings - IEEE International Conference on Robotics and Automation
JF - Proceedings - IEEE International Conference on Robotics and Automation
SN - 1050-4729
M1 - 6907663
ER -