Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/2498
Title: | CNN based sensor fusion method for real-time autonomous robotics systems | Authors: | Yıldız, Berat Durdu, Akif Kayabaşı, Ahmet Duramaz, Mehmet |
Keywords: | Autonomous robotic systems deep learning convolutional neural networks sensor fusion Multiple Algorithm Camera |
Publisher: | Tubitak Scientific & Technical Research Council Turkey | Abstract: | Autonomous robotic systems (ARS) serve in many areas of daily life. The sensors have critical importance for these systems. The sensor data obtained from the environment should be as accurate and reliable as possible and correctly interpreted by the autonomous robot. Since sensors have advantages and disadvantages over each other they should be used together to reduce errors. In this study, Convolutional Neural Network (CNN) based sensor fusion was applied to ARS to contribute the autonomous driving. In a real-time application, a camera and LIDAR sensor were tested with these networks. The novelty of this work is that the uniquely collected data set was trained in a new CNN network and sensor fusion was performed between CNN layers. The results showed that CNN based sensor fusion process was more effective than the individual usage of the sensors on the ARS. | URI: | https://doi.org/10.3906/elk-2008-147 https://hdl.handle.net/20.500.13091/2498 |
ISSN: | 1300-0632 1303-6203 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections Teknik Bilimler Meslek Yüksekokulu Koleskiyonu TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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File | Size | Format | |
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elk-30-1-6-2008-147.pdf | 4.73 MB | Adobe PDF | View/Open |
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