数据增量对循环神经网络的九轴IMU基定向估计准确度的影响
1Inertial Motion Capture Lab, School of ICT, Robotics & Mechanical Engineering, Hankyong National University, Anseong 17579, Republic of Korea.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
数据增强显著改善了基于九轴惯性测量单元 (IMU) 的定向估计神经网络. 虚拟旋转增强被证明是最有效的,超过偏差和噪声添加技术,以提高移动捕捉的准确性.
科学领域:
- 机器人和机器学习 机器人和机器学习
- 传感器融合和惯性导航
背景情况:
- 使用九轴惯性测量单元 (IMU) 的三维 (3D) 定向估计对于惯性运动捕获至关重要.
- 深度学习模型 (神经网络,NN) 显示出对方向估计的前景,但需要大量的数据集.
- 有限的培训数据阻碍了开发基于IMU的导向估计强大的NN.
研究的目的:
- 调查数据增强技术对基于IMU的定向估计神经网络性能的影响.
- 分析虚拟旋转,偏差加法和噪声加法增强方法的有效性,单独和组合.
主要方法:
- 选择了三个数据增强技术:虚拟旋转,偏差添加和噪声添加.
- 在循环神经网络上评估了七种增强组合,用于基于IMU的定向估计.
- 分析了每个增强技术对估计准确性的影响.
主要成果:
- 使用虚拟旋转的增强,单独或与偏差和噪声相结合,始终产生了前四个最佳估计准确度.
- 与偏差和噪音增强相比,旋转增强对性能产生了显著更大的积极影响.
- 旋转,偏差和噪声增强的组合显示出显著的性能改善.
结论:
- 虚拟旋转是增强基于IMU的定向估计神经网络的最有效的数据增强技术.
- 应用旋转增强可以大大提高方向估计NN的性能.
- 这些发现为开发更强大的基于IMU的导向估计系统提供了宝贵的见解.
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