## 1 Introduction
[24], unlike pure stereo event-based visual localization [25], designed a tightly coupled system based on stereo event cameras and IMU, enabling stable operation in challenging environments. Event-based visual SLAM demonstrates impressive performance in high-speed motion scenarios but also induces issues, such as sparse data. Additionally, neural networks are playing an increasingly important role in V-SLAM. In Reference [26], the authors proposed a semantic SLAM method for dynamic scenes, where they designed a static semantic keyframe selection strategy based on segmentation results to mitigate the impact of dynamic objects. [PERSON] et al. [27] integrated visual, inertial, and semantic information for final localization. [PERSON] et al. [4] proposed a deep SLAM network that directly integrates visual and IMU data. Incorporating neural networks provides more robust feature-extraction capabilities than traditional SLAM methods, making them more adaptable to complex environments, such as low-texture scenes. However, neural-network-based methods rely heavily on training data, have high computational requirements, and involve a more complicated system. The methods above are primarily based on point features. [PERSON] et al. [28] innovatively combined point and line features in ORB-LINE-SLAM, improving the localization accuracy in challenging environments. In Reference [29], the authors used vertical line features and point features to achieve promising results in underground parking lots. Combining line features enhances geometric constraints but also introduces challenges, such as the instability of line features and increased complexity in matching. During the operation of visual systems, the proportion of the target's appearance in the image is unknown. A more effective method for extracting target features is to generate an image pyramid composed of images at different scales, scanning the target layer-by-layer [30]. The image pyramid is extensively used in feature extraction to adaptively extract features from objects at different scales within the image [31]. Higher-level features exhibit lower repeatability, offering limited benefits in localization accuracy, but they are still advantageous for feature matching and orientation estimation [32]. In practical applications, the number of pyramid levels and the scale factor are related to feature selection and the image size. The most time-consuming part of the front-end process in ORB SLAM3 is the multi-level feature extraction based on the image pyramid. [PERSON] et al. [33] designed a high-performance hardware accelerator for ORB feature extraction. In engineering applications, ORB feature extraction is often processed in parallel using multithreading to improve the runtime efficiency. These methods mentioned above for accelerating feature extraction impose high hardware requirements, hindering their applicability and scalability. While ORB features exhibit desirable performance and the effectiveness of the image pyramid has been validated, due to performance constraints, low-performance intelligent devices, like drones, primarily use FAST features at the front end [34]. For instance, [PERSON] et al. [35] developed a computationally efficient visual-inertial odometry system by integrating FAST with optical flow techniques. This approach is particularly suitable for visual localization in drones due to its low computational requirements. Neural-network-based methods require the design of complex systems and the collection of large amounts of data, making their implementation challenging in most applications. We hope to propose an efficient method that enhances the runtime efficiency of visual SLAM systems while maintaining stable localization accuracy. Loop-closure detection can provide spatial constraints between previous and current frames, effectively improving mapping consistency. Recent research primarily focuses on detecting loop closure in visual SLAM by constructing descriptors (hand-engineered features).