## 1 Introduction  
Simultaneous Localization and Mapping (SLAM) enables unmanned devices, like autonomous vehicles and drones, to perceive their surrounding environment. In practical applications, the key performance indicators for SLAM include the mapping consistency, localization accuracy, and operational efficiency, which are also the focus of ongoing research. Depending on the primary sensor, SLAM can be broadly divided into Visual SLAM (V-SLAM) [1] and LiDAR SLAM [2]. Visual SLAM is typically favored in environments rich in visual features, while LiDAR SLAM excels in structured environments and performs well even under low-light conditions. Among the cutting-edge visual SLAM methods, ORB-SLAM3 [3] and DVI-SLAM [4] stand out. In LiDAR SLAM, widely used methods include graph optimization-based LOCUS 2.0 [5] and filter-based Point-LIO [6]. As mentioned, SLAM is widely applied across various devices. Autonomous vehicles rely on SLAM for localization and mapping in dynamic environments to ensure safe navigation, particularly in low-speed or enclosed areas, such as mining sites [7], where SLAM plays a crucial role. Single drones or multi-drone collaborations can utilize SLAM to autonomously explore and map unknown terrains [8], which is vital for search and rescue missions in environments where satellite navigation is unavailable. In robotics, such as indoor service robots [9] and construction robots [10], SLAM enables navigation in both indoor and outdoor environments. With advanced decision-making and control technologies [11], SLAM allows these robots to perform complex and diverse tasks. Visual SLAM, compared to LiDAR SLAM, is more cost-effective and has higher practical value, making it a consistent area of research focus. Visual SLAM has continuously developed, with major SLAM systems differing primarily in pose estimation and image processing approaches [12]. Pose estimation methods include filter-based and keyframe-based approaches. Regarding image processing, there are two main methods: direct and feature-based. Mono-SLAM [13] is one of the earliest SLAM systems, employing Shi-Tomasi points and an Extended Kalman Filter (EKF) for pose estimation. Subsequent method improvements have largely retained the fundamental principles of Mono-SLAM. Filter-based methods use only current frame information, which, while computationally efficient, results in limited accuracy. Keyframe-based Bundle Adjustment (BA) is widely used in visual systems, with PTAM [14] and SVO [15] employing BA for estimating the pose between successive frames, offering higher accuracy compared to filter-based methods. Direct methods do not require feature extraction; they utilize photometric information. In Reference [16], [PERSON] et al. introduced the concept of reusing existing map information in direct V-SLAM, achieving improved localization accuracy. Widely used features in feature-based SLAM include oriented FAST and rotated BRIEF (ORB) [17; 18], scale-invariant feature transform (SIFT) [19], speed-up robust features (SURF) [20], and Self-supervised interest point (SURPOINT) [21]. ORB SLAM3 [3] is an advanced and comprehensive visual SLAM system based on ORB features. It supports monocular, stereo, and RGB-D cameras and can be tightly coupled with an Inertial Measurement Unit (IMU) to enhance environmental adaptability. Additionally, ORB SLAM3 offers map reuse, loop closure, and re-localization capabilities to ensure mapping consistency. Compared to ORB SLAM2 [22], ORB SLAM3 features multiple improvements, such as utilizing an atlas to retain all maps, enabling map merging after loop closure, and enhancing loop-closure detection by verifying the local consistency of co-visible frames, thus improving recall. In Reference [3], the authors compared mainstream V-SLAM and Visual Inertial Odometry (VIO) methods, such as DSM [16] and VINS-Fusion [23], demonstrating that ORB SLAM3 is one of the most accurate and robust methods. The emergence of event cameras has introduced new approaches to the front-end of SLAM systems. [PERSON] et al.