Schmiemann, Julian, Schattenberg, Jan, and Frerichs, Ludger
Subjects
DEEP learning, CLOUD computing, GLOBAL Positioning System, AGRICULTURAL industries, ARTIFICIAL neural networks
Abstract
In this work we investigate the possibility to use state of the art approaches for deep learning on point clouds for matching segments, sensed from objects under varying perspectives, with the aim to obtain positional information for relative positioning. Therefore we propose a method and evaluate it using a custom real world data set. [ABSTRACT FROM AUTHOR]
Published
2018
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