菲尔网:使用控制网络进行基于图像的快速室内定位
Sikang Liu1,2, Zhao Huang3, Jiafeng Li2
1School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了FILNet,这是一种使用控制网络的快速室内定位方法. 它显著提高了功能匹配的回忆和速度,改善了现实应用的本地化准确性.
科学领域:
- 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
- 室内定位局部化 室内定位局部化
背景情况:
- 准确的室内定位对于各种应用至关重要.
- 现有的基于图像的方法在特征匹配速度和准确性方面面临挑战.
- 视角转换和特征匹配效率阻碍了性能.
研究的目的:
- 设计一个基于图像的快速室内定位方法 (FILNet).
- 为了提高本地化准确度和减少特征匹配持续时间.
- 解决特征匹配和图像视角转换方面的挑战.
主要方法:
- 开发了一种两阶段的FILNet算法:离线数据库构建和在线本地化.
- 使用控制网络和视觉惯性测距 (VIO) 进行特征指纹.
- 实现了亲属不变性增强算法和快速空间索引方法.
- 采用同谱矩阵波器模型进行特征匹配验证.
主要成果:
- 菲尔尼特改善了特征匹配回忆,从26%提高到57% (角偏差<60度).
- 与K-D树相比,平均特征匹配时间从30.3ms减少到12.7ms.
- 将局部化误差<0.1m的图像百分比从31.61%增加到55.89%.
结论:
- 在基于图像的室内定位方面,FILNet提供了显著的改进.
- 该方法增强了亲属不变性,特征匹配速度和定位精度.
- 在现实场景中,FILNet表现出强大的性能.
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