## 1 Introduction
[14] offered an adaptive Kalman filter low-sampling-rate Global Navigation Satellite System (GNSS) speed and position measurement method based on Inertial Measurement Unit (IMU). [PERSON] et al. [15] proposed a kinematic model-based VSA estimation method by fusing the information from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU). With the diversification and requirements of simultaneous localization and map-building application scenarios, academics have paid increasing attention to the development of SLAM algorithms [16]. Researchers are committed to designing efficient versions of SLAM algorithms suitable for different hardware platforms to meet the performance requirements in different environments and application scenarios [17], and graph-optimization algorithms are gradually being applied. [PERSON] et al. [18] proposed an optimization method based on stochastic gradient descent (SGD), where iteratively, the non-repeatedly randomly selecting constraints and calculating the corresponding gradient descent direction, in which the optimal solution of the objective function is found, can effectively correct the bitmap. [PERSON] et al. [19] extended [PERSON]'s algorithm--a novel node parameterization is applied to the graph, which significantly improves the performance. The ability to handle arbitrary network topologies allows the binding of the complexity of the algorithm to the size of the mapping region rather than to the length of the trajectory. One of the most representative open-source SLAM algorithms for graph optimization is the Cartographer algorithm proposed by Google, which adjusts the bit pose of the submap on the path using loopback detection [20], thus removing the accumulated error in the process of building the graph from the last loopback to the current moment while achieving a balance between computational effort and real-time performance. In the literature [21],an adaptive multi-stage distance scheduler is used to optimize the processing speed of the Cartographer, and the local positional correction is achieved by controlling the scanning range of the LiDAR sensor and the search window of the point-cloud matcher through the scheduling algorithm. The algorithm effectively increases the processing speed of the Cartographer to a level similar to that of close-range LiDAR scanning while maintaining the accuracy of long-range scanning. [PERSON] and [PERSON] et al. [22] introduced point-cloud intensity information into SLAM systems and proposed a novel SLAM framework by adding intensity constraints in the front-end odometry and back-end optimization phases, which uses frame-to-map matching to compute the optimal bit position by minimizing the geometric residuals and intensity residuals, which provides higher accuracy compared to purely geometric SLAM methods. Still, LOAM-based algorithms must perform downsampling operations of the point-cloud data in the execution phase, which does not accurately represent the underlying local network structure. In the autonomous driving technology field, the sensors' localization algorithm, one of the most basic and essential aspects, has received increasingly extensive attention [23]. With the continuous development of deep-learning technology, many scholars have now studied deep-learning-based SLAM schemes to obtain better accuracy and stronger robustness. Deep-learning-based SLAM technology has made many breakthroughs, especially in feature extraction, and matching has shown excellent performance [24]. [PERSON] et al. [25] implemented geometric constraints in the odometry framework to decompose bit position estimation into two parts: the learning-based matching network part and the rigid transform estimation part based on singular value decomposition, and the experimental results show that the method has comparable performance with the traditional LiDAR odometry method. [PERSON] et al.