# Visual-Inertial Navigation System Based on Virtual Inertial Sensors
## 1 Introduction  
Simultaneous localization and mapping (SLAM) is critical technology in mobile navigation, enabling robots to create environmental maps and achieve autonomous localization by utilizing sensors mounted on them [1]. In recent years, SLAM systems with complementarity have been developed to meet the higher demands of mobile platform application scenarios. Among them, the most representative system is the fusion of laser-inertial [2] and visual-inertial sensors [3]. However, when laser radar is used as the primary sensor, it may lack certain directional constraints in scenes with sparse structural features. This is due to its inability to distinguish geometrically continuous repeated scenes, which can result in unreliable motion estimation and significant errors [4]. In contrast, visual-inertial SLAM systems utilizing visual sensors have the advantages of lower cost and smaller size [5]. Furthermore, the fusion of inertial and other sensors in visual-inertial SLAM systems has been demonstrated to improve their accuracy and robustness [6], making them the mainstream combination method in the field of visual SLAM. Visual-inertial SLAM systems can be classified into two forms, loosely coupled and tightly coupled, as discussed in the literature [7]. The loosely coupled approach, exemplified by [PERSON] et al. 's 2012 strategy based on optical flow and inertial constraints, addresses certain mapping challenges. However, it may lead to decreased mapping accuracy due to visual measurement errors [8]. Recently, more progress has been made in the research of deep learning-based visual-inertial fusion methods in the field of visual-inertial navigation. HVIONet (a deep learning-based hybrid visual-inertial odometry approach) is a low-cost visual-inertial fusion method that integrates inertial and image data using deep learning frameworks to improve the speed and accuracy through feature fusion. However, it requires ground-truth data and has high computational costs, limiting its practical application [9]. VIONet (a visual-inertial image-odometry network) utilizes a deep learning method to fuse data from both sources, converting features in the fully connected layer to poses through Gaussian regression and enhancing stability using Savitzky-Golay filters for pose estimation. While this method achieves more accurate pose estimation, real-time synchronization of pose estimation with UAV motion remains an area for improvement. Another emerging deep learning-based fusion method is EMA-VIO, which incorporates external memory attention for efficient and effective pose estimation. However, this method's model requires GPU training, restricting its use in micro-navigation, and further stability verification is needed in open environments [10; 11]. We have developed a new sensor through deep learning to enhance data acquisition and improve accuracy. By pre-collecting data through virtual inertial sensors, we can efficiently process real-time data and perform experiments on GPU platforms. Additionally, in scenarios with low texture, lighting variations, fast motion, or large-scale environments, the visual-inertial SLAM system may encounter tracking failures, leading to errors in state estimation. Furthermore, external factors such as weather conditions and occlusions can also affect the mapping accuracy of the system. When deployed on mobile platforms, the reliability of IMU observations can be compromised due to complex motion behavior. Incorporating multiple IMUs to assist in measurements increases power consumption, complexity, and potential data loss. Therefore, establishing the relationship between the inertial sensor measurement on the carrier through deep learning and constructing virtual inertial sensors to replace the physical IMU on the carrier presents an effective solution to simplify the system. This paper proposes a visual-inertial navigation SLAM system that utilizes a DeepConvGRU neural network model to classify IMU gait data and map the virtual IMU (VIMU) data through a DenseNet-GRU hybrid model.