TY  - CONF
AU  - Petrović, Milica
AU  - Mystkowski, A.
AU  - Jokić, Aleksandar
AU  - Dokić, L.
AU  - Miljković, Zoran
PY  - 2020
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3446
AB  - For the implementation of stereo image-based visual servoing algorithm in the eye-in-hand robotics applications, one of the main concerns is the accurate point feature detection and matching algorithm. Since the visual servoing is carried out in the textureless environment, the feature detection process is even more challenging. To fulfill the requirement of a robust and reliable point feature detection process, in this paper we present the novel deep learning-based algorithm. The approach based on convolutional neural networks and algorithm for detection of manufacturing entities is proposed and detected regions of interest are utilized for the improvement of the point feature detection algorithm. The proposed algorithm is experimentally evaluated in real-world settings by using wheeled nonholonomic mobile robot RAICO equipped with stereo vision system. The experimental results show the improvement of 58% in the accuracy of matched point features in the images obtained during the visual servoing process. Moreover, with the implementation of the proposed deep learning-based approach, the number of successful experimental runs has increased by 80%.
PB  - Institute of Electrical and Electronics Engineers Inc.
C3  - 15th International Conference Mechatronic Systems and Materials, MSM 2020
T1  - Deep learning-based algorithm for mobile robot control in textureless environment
DO  - 10.1109/MSM49833.2020.9201666
ER  - 

@conference{
author = "Petrović, Milica and Mystkowski, A. and Jokić, Aleksandar and Dokić, L. and Miljković, Zoran",
year = "2020",
abstract = "For the implementation of stereo image-based visual servoing algorithm in the eye-in-hand robotics applications, one of the main concerns is the accurate point feature detection and matching algorithm. Since the visual servoing is carried out in the textureless environment, the feature detection process is even more challenging. To fulfill the requirement of a robust and reliable point feature detection process, in this paper we present the novel deep learning-based algorithm. The approach based on convolutional neural networks and algorithm for detection of manufacturing entities is proposed and detected regions of interest are utilized for the improvement of the point feature detection algorithm. The proposed algorithm is experimentally evaluated in real-world settings by using wheeled nonholonomic mobile robot RAICO equipped with stereo vision system. The experimental results show the improvement of 58% in the accuracy of matched point features in the images obtained during the visual servoing process. Moreover, with the implementation of the proposed deep learning-based approach, the number of successful experimental runs has increased by 80%.",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
journal = "15th International Conference Mechatronic Systems and Materials, MSM 2020",
title = "Deep learning-based algorithm for mobile robot control in textureless environment",
doi = "10.1109/MSM49833.2020.9201666"
}

Petrović, M., Mystkowski, A., Jokić, A., Dokić, L.,& Miljković, Z.. (2020). Deep learning-based algorithm for mobile robot control in textureless environment. in 15th International Conference Mechatronic Systems and Materials, MSM 2020
Institute of Electrical and Electronics Engineers Inc...
https://doi.org/10.1109/MSM49833.2020.9201666

Petrović M, Mystkowski A, Jokić A, Dokić L, Miljković Z. Deep learning-based algorithm for mobile robot control in textureless environment. in 15th International Conference Mechatronic Systems and Materials, MSM 2020. 2020;.
doi:10.1109/MSM49833.2020.9201666 .

Petrović, Milica, Mystkowski, A., Jokić, Aleksandar, Dokić, L., Miljković, Zoran, "Deep learning-based algorithm for mobile robot control in textureless environment" in 15th International Conference Mechatronic Systems and Materials, MSM 2020 (2020),
https://doi.org/10.1109/MSM49833.2020.9201666 . .