Alqobali Raghad, Alnasser Reem, Rashidi Asrar, Alshmrani Maha, Alhmiedat Tareq
Saudi Data and AI Authority, Riyadh 12382, Saudi Arabia.
Information Technology Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia.
Sensors (Basel). 2024 Oct 17;24(20):6691. doi: 10.3390/s24206691.
Although grid maps help mobile robots navigate in indoor environments, some lack semantic information that would allow the robot to perform advanced autonomous tasks. In this paper, a semantic map production system is proposed to facilitate indoor mobile robot navigation tasks. The developed system is based on the employment of LiDAR technology and a vision-based system to obtain a semantic map with rich information, and it has been validated using the robot operating system (ROS) and you only look once (YOLO) v3 object detection model in simulation experiments conducted in indoor environments, adopting low-cost, -size, and -memory computers for increased accessibility. The obtained results are efficient in terms of object recognition accuracy, object localization error, and semantic map production precision, with an average map construction accuracy of 78.86%.
尽管网格地图有助于移动机器人在室内环境中导航，但有些缺乏语义信息，无法让机器人执行高级自主任务。本文提出了一种语义地图生成系统，以促进室内移动机器人的导航任务。所开发的系统基于激光雷达技术和基于视觉的系统的应用，以获得具有丰富信息的语义地图，并且在室内环境中进行的模拟实验中，使用机器人操作系统（ROS）和你只看一次（YOLO）v3目标检测模型进行了验证，采用低成本、小尺寸和低内存的计算机以提高可及性。在目标识别准确率、目标定位误差和语义地图生成精度方面，所获得的结果是高效的，平均地图构建准确率为78.86%。
