Inertial sensors are widely used in a variety of applications. 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 measurements 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 resulting in poor heading angle estimation. Therefore, applications that estimate the heading angle of moving objects, such as walking pedestrians, closets, and refrigerators, are prone to error. 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.

翻译：惯性传感器广泛应用于各类应用中。姿态估计是一项常见任务。为应对此类任务，通常采用姿态与航向参考系统算法。该算法依赖陀螺仪读数，利用加速度计测量值更新姿态角，并借助磁力计测量值更新航向角。在室内环境中，磁力计易受干扰导致性能下降，进而造成航向角估计精度不足。因此，针对移动物体（如步行行人、橱柜和冰箱）的航向角估计应用易产生误差。为规避此类问题，本文提出DoorINet——一种端到端的深度学习框架，通过门载低成本惯性传感器（无需磁力计）计算航向角。为评估本方法，我们采集了包含391分钟的加速度计与陀螺仪测量数据及对应真实航向角的独特数据集。实验表明，我们提出的方法优于常用的基于模型的方法与数据驱动方法。