有效的地下道位置识别算法基于最远点子样本和双重注意力变压器
Xinghua Chai1, Jianyong Yang1, Xiangming Yan2
154th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
一个新的双重注意力变压器网络 (DAT-Net) 改善了在无GPS道中的自主位置识别. 这种高效的算法通过有效处理点云数据来提高准确性,以实现可靠的导航.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在没有GPS的环境中,如地下道,自主导航至关重要.
- 现有的位置识别算法与道中常见的稀疏和杂的点云数据作斗争.
- 确保高识别准确性和稳定性是一个重大挑战.
研究的目的:
- 为地下道提出一个高效的点云位置识别算法,双注意力变压器网络 (DAT-Net).
- 在有限的地下道数据中增强有效特征的利用.
- 提高自主位置识别系统的准确性和稳定性.
主要方法:
- 实现了最远点下方采样模块,以减少点云的大小并保留基本的形状信息.
- 开发了一种双重注意力变压器模块,采用多头自我注意力来进行本地信息交换.
- 整合了局部描述符与全球描述符,使用特征融合层进行强大的全球特征表示.
主要成果:
- 拟议的DAT-Net在SubT-Tunnel数据集上获得了0.841的平均F1评分.
- 与现有的最先进的算法相比,证明了更高的识别准确性.
- 在具有挑战性的道环境中,在位置识别测试中展示了增强的稳定性.
结论:
- DAT-Net提供了一种有效的解决方案,用于在GPS无法访问的地下道中进行自主位置识别.
- 该算法的双重注意力机制和功能融合增强了稀疏点云数据的处理.
- 这些发现表明,在地下环境中,强大而准确的机器人导航取得了重大进展.
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