Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural
  Network Based Approach   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.

5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
30
The categories of this paper are: q-alg