Many Internet of Things applications utilize low-cost, micro, electro-mechanical inertial sensors. A common task is orientation estimation. To tackle such a task, attitude and heading reference system algorithms are applied. Relying on the gyroscope readings, the accelerometer readings are used to update the attitude angles, and magnetometer measurements are utilized to update the heading angle. In indoor environments, magnetometers suffer from interference that degrades their performance. This mainly influences applications focused on estimating the heading angle like finding the heading angle of a closet or fridge door. To circumvent such situations, we propose DoorINet, an end-to-end deep-learning framework to calculate the heading angle from door-mounted, low-cost inertial sensors without using magnetometers. To evaluate our approach, we record a unique dataset containing 391 minutes of accelerometer and gyroscope measurements and corresponding ground-truth heading angle. We show that our proposed approach outperforms commonly used, model based approaches and data-driven methods.

利用低成本的惯性传感器，我们提出了一种名为DoorINet的端到端深度学习框架，用于从安装在门上的惯性传感器中计算方向角，而不使用磁强计干扰，以解决室内环境下磁强计性能下降的问题。通过对包含391分钟加速度计和陀螺仪测量值以及相应地面真实方向角的独特数据集的评估，我们证明了我们的方法胜过常用的基于模型的方法和数据驱动方法。

DoorINet: 门设于门体物联网应用的深度学习惯性框架