使用微机电系统(MEMS)惯性测量单元(IMU)进行可靠且准确的姿态和航向估计是确定各种下游应用精度的最关键技术,尤其是行人航位推算(PDR),人体运动跟踪,和微型飞行器(MAV)。然而,姿态和航向参考系统(AHRS)的准确性通常会受到低成本MEMS-IMU的嘈杂性质的影响,动态运动引起的大外部加速度,和无处不在的磁扰。为了应对这些挑战,我们提出了一种新颖的数据驱动的IMU校准模型,该模型采用时间卷积网络(TCN)对随机误差和扰动项进行建模,提供去噪传感器数据。对于传感器融合,我们使用扩展互补滤波器(ECF)的开环解耦版本来提供准确和可靠的姿态估计。我们提出的方法使用三个公共数据集进行了系统评估,TUMVI,EuRoCMAV,和OxIOD,使用不同的IMU设备,硬件平台,运动模式,和环境条件;它在两个指标上优于先进的基线数据驱动方法和互补过滤,即绝对姿态误差和绝对偏航误差,分别超过23.4%和23.9%。泛化实验结果证明了我们的模型在不同设备和使用模式上的鲁棒性。
Robust and accurate attitude and heading estimation using Micro-Electromechanical System (MEMS) Inertial Measurement Units (IMU) is the most crucial technique that determines the accuracy of various downstream applications, especially pedestrian dead reckoning (PDR), human motion tracking, and Micro Aerial Vehicles (MAVs). However, the accuracy of the Attitude and Heading Reference System (
AHRS) is often compromised by the noisy nature of low-cost MEMS-IMUs, dynamic motion-induced large external acceleration, and ubiquitous magnetic disturbance. To address these challenges, we propose a novel data-driven IMU calibration model that employs Temporal Convolutional Networks (TCNs) to model random errors and disturbance terms, providing denoised sensor data. For sensor fusion, we use an open-loop and decoupled version of the Extended Complementary Filter (ECF) to provide accurate and robust attitude estimation. Our proposed method is systematically evaluated using three public datasets, TUM VI, EuRoC MAV, and OxIOD, with different IMU devices, hardware platforms, motion modes, and environmental conditions; and it outperforms the advanced baseline data-driven methods and complementary filter on two metrics, namely absolute attitude error and absolute yaw error, by more than 23.4% and 23.9%. The generalization experiment results demonstrate the robustness of our model on different devices and using patterns.