论文标题
使用注意机制和RES2NET模块，用于行人跟踪的深度学习惯性遗迹
Deep Learning-based Inertial Odometry for Pedestrian Tracking using Attention Mechanism and Res2Net Module
论文作者
论文摘要
由于低成本的惯性传感器误差积累，行人死的估算是一项具有挑战性的任务。最近的研究表明，深度学习方法可以在处理此问题时取得令人印象深刻的绩效。在这封信中，我们使用基于深度学习的速度估计方法提出了惯性的进程。基于RES2NET模块和两个卷积块注意模块的深神经网络被利用，以恢复智能手机的水平速度向量与原始惯性数据之间的潜在连接。我们的网络仅使用50％的公共惯性进程数据集（RONIN）数据进行培训。然后，在Ronin测试数据集和另一个公共惯性探针数据集（OXIOD）上进行了验证。与传统的基于系统和标题的基于系统的算法相比，我们的方法将绝对翻译误差（ATE）降低了76％-86％。此外，与最先进的深度学习方法（RONIN）相比，我们的方法将其ATE提高了6％-31.4％。
Pedestrian dead reckoning is a challenging task due to the low-cost inertial sensor error accumulation. Recent research has shown that deep learning methods can achieve impressive performance in handling this issue. In this letter, we propose inertial odometry using a deep learning-based velocity estimation method. The deep neural network based on Res2Net modules and two convolutional block attention modules is leveraged to restore the potential connection between the horizontal velocity vector and raw inertial data from a smartphone. Our network is trained using only fifty percent of the public inertial odometry dataset (RoNIN) data. Then, it is validated on the RoNIN testing dataset and another public inertial odometry dataset (OXIOD). Compared with the traditional step-length and heading system-based algorithm, our approach decreases the absolute translation error (ATE) by 76%-86%. In addition, compared with the state-of-the-art deep learning method (RoNIN), our method improves its ATE by 6%-31.4%.
