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

中文翻译：

---

使用带有 RGBD 图像和 YOLO 算法的对象识别软件进行移动机器人导航

摘要 这项工作提出了一种基于 YOLO 算法的视觉系统，用于识别可能是移动机器人路径中障碍物的静态物体。为了识别物体及其距离，使用了微软 Kinect 传感器。此外，还使用了 Nvidia Jetson TX2 GPU 来提高图像处理算法性能。我们的实验结果表明，YOLO 网络已经检测到所有预定义的障碍物，并具有良好的可靠性，并且使用 Microsoft Kinect 相机返回的深度信息计算距离的误差低于 3.64%。