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Semantic navigation for automated robotic inspection and indoor environment quality monitoring
Automation in Construction ( IF 9.6 ) Pub Date : 2024-12-24 , DOI: 10.1016/j.autcon.2024.105949
Difeng Hu, Vincent J.L. Gan
Automation in Construction ( IF 9.6 ) Pub Date : 2024-12-24 , DOI: 10.1016/j.autcon.2024.105949
Difeng Hu, Vincent J.L. Gan
Maintaining a comfortable indoor environment is essential for enhancing occupant well-being. However, traditional inspection methods rely on manual input of precise coordinates for target objects, limiting efficiency. This paper proposes a semantic navigation approach to improve robotic inspection intelligence and efficiency. A revised RandLA-Net and KNN algorithm construct a semantic map rich in detailed object information, supporting semantic navigation. Subsequently, an object instance reasoning algorithm automatically identifies and extracts target object coordinates from the semantic map using human-like language commands. Given the position information, a semantics-aware A* algorithm calculates safer, more efficient navigation paths through enhanced robot-environment interaction. Experiments demonstrate a position accuracy of ∼0.08 m for objects in the semantic map and effective coordinate extraction by the reasoning algorithm. The semantics-aware A* algorithm generates paths farther from obstacles and cluttered areas with less computational time, indicating its superior performance in terms of the robot's safety and inspection efficiency.
中文翻译:
用于自动化机器人检测和室内环境质量监测的语义导航
保持舒适的室内环境对于提高居住者的幸福感至关重要。然而,传统的检测方法依赖于手动输入目标物体的精确坐标,从而限制了效率。本文提出了一种语义导航方法,以提高机器人检测的智能化和效率。改进的 RandLA-Net 和 KNN 算法构建了富含详细对象信息的语义图,支持语义导航。随后,对象实例推理算法使用类似人类的语言命令从语义图中自动识别并提取目标对象坐标。给定位置信息,语义感知 A* 算法通过增强的机器人-环境交互计算更安全、更高效的导航路径。实验表明,语义图中物体的位置精度为 ∼0.08 m,推理算法有效提取坐标。语义感知的 A* 算法以更少的计算时间生成远离障碍物和杂乱区域的路径,表明其在机器人的安全性和检查效率方面的卓越性能。
更新日期:2024-12-24
中文翻译:
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用于自动化机器人检测和室内环境质量监测的语义导航
保持舒适的室内环境对于提高居住者的幸福感至关重要。然而,传统的检测方法依赖于手动输入目标物体的精确坐标,从而限制了效率。本文提出了一种语义导航方法,以提高机器人检测的智能化和效率。改进的 RandLA-Net 和 KNN 算法构建了富含详细对象信息的语义图,支持语义导航。随后,对象实例推理算法使用类似人类的语言命令从语义图中自动识别并提取目标对象坐标。给定位置信息,语义感知 A* 算法通过增强的机器人-环境交互计算更安全、更高效的导航路径。实验表明,语义图中物体的位置精度为 ∼0.08 m,推理算法有效提取坐标。语义感知的 A* 算法以更少的计算时间生成远离障碍物和杂乱区域的路径,表明其在机器人的安全性和检查效率方面的卓越性能。