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一个基于磁性惯性传感器的新型浅层神经网络增强姿势估计器,用于拒绝参考环境

Akos Odry¹, Peter Sarcevic¹, Giuseppe Carbone²

¹Faculty of Engineering, University of Szeged, 6725 Szeged, Hungary.

Sensors (Basel, Switzerland)

|November 27, 2025

概括

这项研究通过将磁性,角速率和重力 (MARG) 传感器数据与浅层神经网络 (NN) 融合来增强移动机器人的姿势估计. 这种方法可以在没有GPS的环境中提高位置/速度的准确性,这对于四旋翼导航至关重要.

科学领域:

机器人技术机器人技术机器人技术
传感器融合式传感器
机器学习机器学习

背景情况:

磁性,角速率和重力 (MARG) 传感器是移动机器人姿势估计的标准,但有局限性.
在缺乏绝对引用的环境中仅依赖MARG数据导致了大量的估计不确定性.
准确的姿势估计对于自主导航至关重要,特别是在GPS被拒绝或地标不足的地区.

研究的目的:

通过将MARG传感器数据与浅层神经网络 (NN) 模型融合,提高位置和速度估计的准确性.
开发和训练一个级联前进的NN,以可靠地估计动态系统中的真实加速度.
评估不同NN拓和培训策略的有效性,以改善姿势估计.

主要方法:

开发和训练了三种类型的级联前进的NNs,用于使用MARG测量和信号特征进行加速估计.
在NN训练期间内置了扩展的卡尔曼和梯度下降方向过器.
使用具有六度自由度 (6DOF) 运动捕捉系统的低成本飞行四旋翼飞机进行实验验证.

主要成果:

拟议的NN方法显著提高了基于旋转矩阵的加速向量计算的准确性 (皮尔森相关系数为0.88而基线为0.73).
在10秒间隔 (50米飞行) 内的四旋翼机机动中获得可靠的位置/速度估计,位置误差在2-4米之间.
与基线方法相比,在提高估计准确性方面表现优越.

结论:

开发的基于NN的MARG数据融合提供了一个可行的和有效的解决方案,以提高姿势估计的准确性.
这种方法对于在没有GPS或缺乏地标的环境中运行的移动机器人来说尤其有价值.
未来的研究将专注于将这种方法的应用扩展到未知的环境中.

关键词:

卡尔曼过器可以过.梯度下降过器的梯度下降过器惯性测量单位是一种惯性测量单位.构成估计估计的估计.融合传感器融合传感器融合传感器浅层神经网络是一种神经网络.

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