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在动态运动和磁性干扰下强大的态度和方向估计

Fan Bo^1,2, Jia Li^1,2, Weibing Wang^1,2

¹Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China.

|May 27, 2023

概括

本研究介绍了一种使用时空卷积网络 (TCN) 进行微电子机械系统 (MEMS) 惯性测量单元 (IMU) 校准的新型数据驱动模型. 该方法在行人死亡计算和微型飞行器 (MAV) 等应用中显著提高了姿态和方向估计的准确性.

科学领域:

机器人和导航机器人和导航
传感器融合式传感器
机器学习用于传感器数据.

背景情况:

精确的态度和方向估计对于微电子机械系统 (MEMS) 惯性测量单元 (IMU) 在行人死亡计数 (PDR),人类运动跟踪和微型飞行器 (MAV) 等应用中至关重要.
低成本的MEMS-IMU受到噪音,动态运动和磁性干扰的影响,损害了态度和方向参考系统 (AHRS) 的准确性.
现有的方法在现实条件下与MEMS-IMU固有的不准确性作斗争.

研究的目的:

开发一种新的数据驱动的IMU校准模型,以提高态度和航向估计的准确性和稳定性.
为了应对传感器噪声,外部加速和MEMS-IMU中的磁性干扰所带来的挑战.
为了提高下游应用程序的性能,这些应用程序依赖于精确的运动跟踪.

主要方法:

一个数据驱动的IMU校准模型,利用时间卷积网络 (TCN) 通过模拟随机错误和干扰来消除传感器数据的噪音.
一个开放循环,脱的扩展补充过器 (ECF) 提供强大的传感器融合和准确的态度估计.
使用三个公共数据集 (TUM VI,EuRoC MAV,OxIOD) 进行系统评估,具有多种硬件和运动条件.

主要成果:

拟议的基于TCN的校准和ECF融合方法与先进的基线方法相比,显示出更高的性能.
与最先进的技术相比,实现了超过23.4%的绝对姿态误差和23.9%的绝对偏航误差的改善.
通用化实验证实了该模型在不同IMU设备和使用模式中的稳定性.

结论:

这种新型数据驱动的方法有效地减轻了MEMS-IMU中的噪音和干扰,从而导致更准确的态度和方向估计.
拟议的方法提供了一个强大的和可通用的解决方案,用于在各种具有挑战性的环境中提高AHRS性能.
这项工作为需要精确的运动跟踪和导航的应用提供了重大进展.

关键词:

现在的AHRSRS是什么?在MEMS IMU中,MEMS IMU是一个非常重要的组织.态度态度估计估计补充过器是一个补充过器.深度学习是一种深度学习.磁性干扰是一种磁性干扰.

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