## 2 Related Work  
### Multi-Sensor Fusion  
Multi-sensor fusion is an essential technique for improving the accuracy and robustness of SLAM systems. Combining data from different sensors, such as cameras, LiDAR, and IMU, can help overcome the limitations of individual sensors and enable a more accurate estimation of the robot's pose.  
One approach to multi-sensor fusion involves optimizing and enhancing the original SLAM framework, such as visual SLAM or LiDAR SLAM, by integrating new sensors while keeping the original framework unchanged. For instance, the LiDAR-inertial odometry (LIMO) proposed by [PERSON] et al. [12] enhances the visual odometry based on monocular VO by using depth information from LiDAR.  
Another promising research direction is to reconstruct the SLAM framework to accommodate multiple sensors, which enables fuller exploitation of the advantages of different sensor types. For instance, [PERSON] et al. proposed a laser 2D LiDAR-vision-inertial SLAM approach [13], which is a loosely coupled EKF-based algorithm that requires an upper computer to assist in the computation. Similarly, [PERSON] et al. utilized the error state extended Kalman filter (ESEKF) to fuse multiple sensors of IMU, vision sensors, LiDAR, radar, and GPS in a loosely coupled fusion with some scalability [14]. Additionally, LICFUSION [15] and LICFUSION 2.0 [16] integrate IMU measurements, sparse visual features, and LiDAR features and perform joint state optimization using a multistate constrained Kalman filter (MSCKF) framework. These multi-sensor fusion approaches have shown exciting potential in improving the accuracy and robustness of SLAM systems.  
The factor graph model has been introduced and developed in the field of multi-sensor fusion SLAM. [PERSON] et al. [17] proposed a SLAM framework that integrates the information of multiple sensors including a camera, LiDAR, IMU, and global positioning system (GPS) based on vision-LiDAR calibration. Different sensors are fused in a tightly coupled manner and finally optimized by a factor graph. NF-iSAM [18] leverages the expressive power of neural networks and trains normalizing flows to draw samples from the joint posterior of a non-Gaussian factor graph. By utilizing a Bayes tree, NF-iSAM is able to exploit the sparsity structure of SLAM, enabling efficient incremental updates for SLAM problems. [PERSON] et al. proposed an LVI-SAM [19] method, which is a factor graph-based LiDAR-vision-inertial SLAM method that possesses global optimization based on scene re-identification and can keep working when LiDAR or vision sensors fail. Additionally, [PERSON] et al. proposed a tightly coupled SLAM system [20] that extracts feature from LiDAR and vision to jointly optimize information in a single integrated factor graph, and [PERSON] and [PERSON] et al. proposed R\({}^{3}\)LIVE [21], which constructs geometric structures and renders textures using LiDAR-inertial odometry (LIO) and visual-inertial odometry (VIO), respectively, and can reconstruct dense 3D RGB point clouds of the environment in real time.  
The drawback of the aforementioned multi-sensor fusion methods is their increased computational complexity and potential challenges in real-time processing, particularly when dealing with large-scale scenes. Integrating data from multiple sensors and optimizing across different modalities can lead to higher computational demands, potentially limiting the real-time performance of the SLAM system, especially in resource-constrained environments.