Alhmiedat Tareq, Alia Osama Moh'd
Department of Information Technology, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, 47713, Saudi Arabia.
Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia.
Sci Rep. 2025 Jul 1;15(1):21937. doi: 10.1038/s41598-025-07921-7.
The utilization of semantic knowledge has ushered in a new era in robot navigation and localization, enabling heightened information representation. This paper introduces an enhanced semantic classification system that leverages a cost-effective, low-processing LiDAR unit in conjunction with a proficient deep neural network (DNN) model. Unlike vision-based methods, which are often susceptible to lighting conditions and environmental variability, LiDAR offers more robust and consistent performance in diverse settings. The Robot Operating System (ROS) development environment was employed alongside a two-wheel-drive robot platform to evaluate the system's efficiency and accuracy. The efficacy of the proposed system has been rigorously validated through both simulation studies and real-world scenarios across two distinct experimental testbeds characterized by varying features. Encouragingly, the results obtained showcase a high level of semantic classification accuracy, standing competitively against diverse semantic classification systems. Furthermore, the developed system successfully generated a semantic map of the navigational area with exceptional classification precision.
语义知识的利用开创了机器人导航与定位的新纪元，实现了更高层次的信息表示。本文介绍了一种增强型语义分类系统，该系统利用经济高效、低处理量的激光雷达单元与高效的深度神经网络（DNN）模型相结合。与基于视觉的方法不同，基于视觉的方法往往易受光照条件和环境变化的影响，而激光雷达在各种环境中提供更强大、更一致的性能。机器人操作系统（ROS）开发环境与两轮驱动机器人平台一起用于评估系统的效率和准确性。通过在两个具有不同特征的不同实验测试平台上进行模拟研究和实际场景，对所提出系统的有效性进行了严格验证。令人鼓舞的是，所获得的结果展示了高水平的语义分类准确性，与各种语义分类系统相比具有竞争力。此外，所开发的系统成功生成了具有卓越分类精度的导航区域语义地图。
