# LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
## 1 Introduction
The former optimized the bias by regressing the velocity but still used the corrected IMU data to compute a quadratic integration to obtain the position, while the latter integrated the regressed velocity directly. [PERSON] et al. [PERSON] et al. (2019) used the EKF to fuse the raw IMU data with the relative displacement measurements from the Residual Network. For underground space personnel positioning, the wearable visual-inertial positioning undershirt equipment used in this paper is shown in Figure 1, whose inertial measurement reference will produce a moving base phenomenon with human motion, which will produce positioning error if solved according to the traditional Strapdown inertial navigation algorithm. Therefore, this paper proposes a neural network-assisted visual-inertial personnel positioning method to solve the inertial measurement errors caused by the moving base and improve positioning accuracy. The novelty of this work lies in proposing an LSTM network to regress six-dimensional IMU data and constructing two location loss functions. The regressed six-dimensional IMU data are then fused with visual to improve person localization. The contributions of this paper are as follows. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Methods** & **Test Distance** & **Accuracy Error** & **ATE** \\ \hline
[18] & incorporates IMU, laser, and visual & 185 m & 0.41\% & X \\ \hline
[19] & incorporates IMU, laser, and visual & 950 m & 0.55\% & X \\ \hline
[20] & Particle filtering method to & & & \\  & combine inertial measurement & 340 m & 0.75\% & X \\  & units on monocular camera hinges & & & \\ \hline
[21] & selection of the best effect switch & & & \\  & between VIO and PDR & & & \\ \hline
[22] & PDR-aided visual-inertial & X & X & 0.635 m \\ \hline
[23] & a visual-inertial odometry assisted & & & \\  & by pedestrian gait information for & & & \\  & smartphone-based & & & \\ \hline
[24] & fusion of foot-mounted IMU and & & & \\  & stereo camera & X & X & 0.227 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pedestrian positioning method and accuracy. X means not mentioned. Regression of 6-dimensional inertial measurement data from the IMU data fragment under the moving base using LSTM neural network. The problem of inertial measurement error caused by the moving base is eliminated. Then, the point features are closely coupled with the inertial measurement data using vision as the observation quantity to optimize the mechanism and improve the personnel positioning accuracy. A relative position loss function and an absolute position loss function are designed. The nature of inertial data itself is used to constrain: position information can be obtained after two integrations. Thus, the loss of neural network learning is reduced. The remainder of this paper is organized as follows. Section 2 discusses in detail the general framework of the system, the basic mathematical description of visual-inertial, the inertial measurement error model under the dynamic base, and the neural network-assisted visual-inertial fusion localization method. Section 3 contains experiments on inertial measurement error under a dynamic base and experiments on personnel localization in an indoor-outdoor environment simulating an underground space, followed by the conclusions in Section 4.