# LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
## 1 Introduction  
With the continuous development of modern society, the development process of underground space is accelerating, and people spend more than 90% of their time living and working indoors or underground. Globally, there are more than 1 million emergencies such as earthquakes, fires, and mining accidents every year, which can cause huge casualties and material losses when the events occur and during the rescue process. Therefore, the demand for public safety, emergency rescue, and other fields is driving the explosive growth of high-precision and high-efficiency navigation and location services in underground space. The commonly used underground space localization techniques include UWB (Ultra Wide Band) localization based on wireless signals, WLAN (Wireless Local Area Networks), and infrared, as well as sensor-based IMU localization and visual localization. Wireless signal-based localization methods are highly susceptible to occlusion and interference in underground space and have poor stability. Methods using inertial sensor localization, such as the PDR algorithm [1; 2; 3], ZUPT algorithm [4], etc., have the advantages of small size, full autonomy, all-weather use, and are not easily affected by external environmental interference and high update frequency. However, the single inertial sensor will drift due to quadratic integration and changes in human physique and walking posture. The visual positioning method has the advantages of relatively low cost, high positioning accuracy, and no cumulative error under long-time measurement [5]. However, the camera will have motion blur and feature-matching failure when it moves too fast or is obscured. It is difficult to achieve high-accuracy positioning in underground space using a single positioning method [6]. A combined positioning approach that incorporates multiple sensors can make full use of the complementary advantages between sensors to improve the accuracy and robustness of positioning [7]. The observation information using vision can effectively estimate and correct the accumulated errors in IMU readings, while for fast movements in a short period, inertial sensors can provide some better estimates and maintain a better estimate of the position even during the period when the vision is obscured, resulting in invalid data; therefore, the positioning approach using a combination of vision and IMU has advantages. Depending on the back-end optimization, research on visual-inertial localization [8; 9] can be divided into filter-based and optimization-based approaches. The following categories of filter-based methods are available: extended Kalman filter (EKF)-based algorithms such as MonoSLAM [10]; particle filter-based methods such as FastSLAM [11] and its monocular SLAM alternative; multi-state constrained Kalman filter (MSCKF) [12; 13]; and stereo multi-state constrained Kalman filter(S-MSCKF) [14]. The filter-based approach estimates both the pose of the camera and the position of the landmark in a state vector, which is a potential source of scalability inefficiency and the Markovian nature of the filter-based approach, i.e., the inability to establish the relationship between a moment and all previous states, so the mainstream visual-inertial tight coupling research is currently based on a nonlinear optimization approach. SVO [15] is a semi-direct visual odometry, which combines the feature point method and the direct method, and finally calculates the bit pose based on optimization. The Flying Robotics Laboratory of the Hong Kong University of Science and Technology [16] proposed VINS-mono (visual-inertial system), a tightly coupled monocular visual-inertial SLAM system with a better system framework, including five parts: observation preprocessing, initialization, joint local visual-inertial optimization, global map optimization, and closed-loop detection. [PERSON] et al. proposed the ORB-SLAM3 [17] system, which is one of the most accurate and robust algorithms available.