ICRA 2012 Paper Abstract


Paper TuD210.3

Fehr, Duc (University of Minnesota), Cherian, Anoop (U. of Minnesota), Sivalingam, Ravishankar (University of Minnesota), Nickolay, Sam (UMN), Morellas, Vassilios (U. of Minnesota), Papanikolopoulos, Nikos (University of Minnesota)

Compact Covariance Descriptors in 3D Point Clouds for Object Recognition

Scheduled for presentation during the Interactive Session "Interactive Session TuD-2" (TuD210), Tuesday, May 15, 2012, 17:00−17:30, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on November 14, 2018

Keywords Recognition, Computer Vision for Robotics and Automation, Visual Learning


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.



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