In this paper, we introduce a dictionary learning framework using RGB-D covariance descriptors on point cloud data for performing object classification. Dictionary learning in combination with RGB-D covariance descriptors provides a compact and flexible description of point cloud data. Furthermore, the proposed framework is ideal for updating and sharing dictionaries among robots in a decentralized or cloud network. This work demonstrates the increased performance of 3D object classification utilizing covariance descriptors and dictionary learning over previous results with experiments performed on a publicly available RGB-D database.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings - IEEE International Conference on Robotics and Automation|
|State||Published - Jun 29 2015|
|Event||2015 IEEE International Conference on Robotics and Automation, ICRA 2015 - Seattle, United States|
Duration: May 26 2015 → May 30 2015
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© 2015 IEEE.