MGLD-TLNet:用于输电线路检查的多重几何和远距离代表网络
IEEE transactions on cybernetics
|February 16, 2026
概括
本研究介绍了用于传输线 (TL) 检查的3D感知方法,提高了复杂环境中的精度. 该方法有效地处理稀疏数据和结构相关性,以提供可靠的电力供应.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 有效的输电线路 (TL) 检查对于可靠的电力供应至关重要.
- 复杂的走廊环境给当前的检查方法带来了挑战,特别是稀疏和不平衡的点云数据.
研究的目的:
- 介绍一种新的基于3D的感知方法,用于在具有挑战性的环境中进行TL检查.
- 解决TL检查任务中固有的数据稀疏性,不平衡性和结构相关性.
主要方法:
- 建模远程空间关系以减轻数据稀疏性和不平衡.
- 构建一个统一的3D表示,共同建模塔,导体和植被.
- 通过几何意识对齐,融合了笛卡尔几何和极地几何.
主要成果:
- 提出的方法,MGLD-TLNet,显示出更好的稳定性和准确性.
- 在稀疏性,封闭性和复杂的环境相互作用条件下观察到一致的性能增长.
- 在真实世界的走廊数据集上的实验验证实了该方法的有效性.
结论:
- 开发的3D感知方法提高了TL在复杂环境中的检查能力.
- 利用远距离的空间关系和融合的几何表示,提高了稳定性和准确性.
- 这项工作通过先进的检查技术,有助于实现更可靠的电力供应.
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