Mobile Robot Navigation Using an Object Recognition Software with RGBD Images and the YOLO Algorithm

• Published in: Applied artificial intelligence Vol. 33; no. 14; pp. 1290 - 1305
• Main Authors: Dos Reis, Douglas Henke, Welfer, Daniel, De Souza Leite Cuadros, Marco Antonio, Gamarra, Daniel Fernando Tello
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 06-12-2019; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; Barriers; Image processing; Object recognition; Robots; Static objects; Vision systems
• ISSN: 0883-9514; 1087-6545
• DOI: 10.1080/08839514.2019.1684778

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 the Microsoft Kinect camera had an error below of 3,64%.