DoorINet：基于惯性深度学习的门朝向预测
DoorINet: Door Heading Prediction through Inertial Deep Learning
AI总结：
针对室内磁力计干扰导致航向估计误差的问题，提出端到端深度学习框架DoorINet，无需磁力计，仅用门载低成本惯性传感器实现航向预测，并通过独特数据集验证其性能优于传统方法。
摘要：
惯性传感器广泛应用于各类场景，朝向估计是常见任务，通常采用姿态与航向参考系统算法：依托陀螺仪读数，用加速度计数据更新姿态角，磁力计数据更新航向角。但室内环境中磁力计易受干扰，导致航向角估计精度下降，使行人、衣柜、冰箱等移动物体的航向估计应用易出错。为此，我们提出DoorINet——一种端到端深度学习框架，无需磁力计，仅通过门载低成本惯性传感器计算航向角。我们构建了含391分钟加速度计、陀螺仪数据及对应真实航向角的独特数据集，实验表明该方法优于常用的基于模型和数据驱动的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
