Compact Covariance Descriptors in 3D Point Clouds for Object Recognition

被引:0
|
作者
Fehr, Duc [1 ]
Cherian, Anoop [1 ]
Sivalingam, Ravishankar [1 ]
Nickolay, Sam [1 ]
Morellas, Vassilios [1 ]
Papanikolopoulos, Nikolaos [1 ]
机构
[1] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA
关键词
HISTOGRAMS; RANGE;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
One of the most important tasks for mobile robots is to sense their environment. Further tasks might include the recognition of objects in the surrounding environment. Three dimensional range finders have become the sensors of choice for mapping the environment of a robot. Recognizing objects in point clouds provided by such sensors is a difficult task. The main contribution of this paper is the introduction of a new covariance based point cloud descriptor for such object recognition. Covariance based descriptors have been very successful in image processing. One of the main advantages of these descriptors is their relatively small size. The comparisons between different covariance matrices can also be made very efficient. Experiments with real world and synthetic data will show the superior performance of the covariance descriptors on point clouds compared to state-of-the-art methods.
引用
收藏
页码:1793 / 1798
页数:6
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