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
Issue Date: 2022
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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