In this work, we propose a novel method for performing inertial aided navigation, by using deep neural networks (DNNs). To date, most DNN inertial navigation methods focus on the task of inertial odometry, by taking gyroscope and accelerometer readings as input and regressing for integrated IMU poses (i.e., position and orientation). While this design has been successfully applied on a number of applications, it is not of theoretical performance guarantee unless patterned motion is involved. This inevitably leads to significantly reduced accuracy and robustness in certain use cases. To solve this problem, we design a framework to compute observable IMU integration terms using DNNs, followed by the numerical pose integration and sensor fusion to achieve the performance gain. Specifically, we perform detailed analysis on the motion terms in IMU kinematic equations, propose a dedicated network design, loss functions, and training strategies for the IMU data processing, and conduct extensive experiments. The results show that our method is generally applicable and outperforms both traditional and DNN methods by wide margins.
翻译:在这项工作中,我们提出了一个使用深神经网络进行惯性辅助导航的新颖方法。迄今为止,大多数DNN惯性导航方法都侧重于惯性观察测量任务,将陀螺仪和加速度计读数作为综合IMU的成形(即位置和方向)的输入和回归值。虽然这一设计在一些应用中成功应用过,但是它不是理论性能保障,除非涉及模式化运动。这不可避免地导致某些使用案例中的准确性和稳健性大大降低。为了解决这个问题,我们设计了一个框架,用DNNW计算可观测到的IMU集成术语,随后是数字组合和传感器聚合,以实现性能增益。具体地说,我们对IMU的运动术语进行详细分析,为IMU数据处理提出专门的网络设计、损失功能和培训战略,并进行广泛的实验。结果显示,我们的方法一般适用,并且超越了宽幅应用的传统方法和DNNN方法。