Abstract
This work presents a vision system based on the YOLO algorithm to identify static objects that could be obstacles in the path of a mobile robot. In order to identify the objects and its distances a Microsoft Kinect sensor was used. In addition, a Nvidia Jetson TX2 GPU was used to increase the image processing algorithm performance. Our experimental results indicate that the YOLO network has detected all the predefined obstacles for which it has been trained with good reliability and the calculus of the distance using the depth information returned by Microsoft Kinect had an error below of 3,64%.
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Acknowledgment
We would like to thank the National Institute of Space Research (INPE) located at the Federal University of Santa Maria (UFSM), especially Adriano Petry, for their cooperation.
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Henke dos Reis, D., Welfer, D., de Souza Leite Cuadros, M.A., Tello Gamarra, D.F. (2021). Object Recognition Software Using RGBD Kinect Images and the YOLO Algorithm for Mobile Robot Navigation. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds) Intelligent Systems Design and Applications. ISDA 2019. Advances in Intelligent Systems and Computing, vol 1181. Springer, Cham. https://doi.org/10.1007/978-3-030-49342-4_25
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DOI: https://doi.org/10.1007/978-3-030-49342-4_25
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