用图形神经网络进行拓地理定位的子图学习.
1Photogrammetric Computer Vision Lab, Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种新的图形神经网络方法,用于使用运动轨迹进行拓地理定位. 这种方法在地图上精确地确定位置,模仿人类的空间认知.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 空间认知,包括自我定位和导航,需要有效的学习方法.
- 模仿类似人类的空间能力是人工智能和机器人的重大挑战.
研究的目的:
- 建议在地图上进行拓地理定位的新方法.
- 利用运动轨迹和图形神经网络来提高定位精度.
主要方法:
- 将运动轨迹编码为路径子图,其中节点代表转向方向,边缘代表相对距离.
- 在这些子图上训练图形神经网络,用于多类分类以确定位置.
- 在小型,中型和大型地图数据集上测试该方法,使用模拟和现实世界的轨迹.
主要成果:
- 在小型数据集上达到93.61%的准确性,在中型数据集上达到95.33%,在模拟轨迹的大数据集上达到87.50%.
- 通过视觉惯性测距生成的实际轨迹的可比准确性.
- 该方法利用图形神经网络的能力进行有效的空间学习.
结论:
- 拟议的方法为拓地理定位提供了一种高效的学习方法.
- 它只需要一个2D图形图和一个负担得起的传感器来获得相对运动轨迹数据.
- 这种技术通过有效地建模运动轨迹来增强空间认知能力.
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