## 1 Introduction  
Simultaneous localization and mapping (SLAM) is a foundational technology that plays a crucial role in the autonomous navigation of robots, underwater platforms [1], and self-driving vehicles [2]. It enables unmanned systems to accurately determine their position within an unknown environment while simultaneously constructing a detailed map of the surroundings in real-time. By integrating data from various sensors such as LiDAR, cameras, and inertial measurement units (IMUs), SLAM provides a comprehensive understanding of the environment, which is essential for safe and efficient operation. In robotics, visual SLAM is widely utilized due to its cost-effectiveness and versatility, aiding in tasks like industrial automation, exploration, and service delivery. Drones rely on SLAM for obstacle avoidance and mapping in GPS-denied environments, while autonomous vehicles depend on it to navigate and make decisions in complex traffic scenarios. Overall, visual SLAM is indispensable for the development of intelligent systems capable of operating autonomously in the physical world. Visual SLAM can be broadly categorized into two approaches based on the type of visual features: handcrafted feature-based and direct methods. The feature-based approach is widely adopted due to its efficiency and precision. In this method, the SLAM front-end tracks keypoints in adjacent frames and optimizes positioning and mapping using multi-view geometry optimization techniques. However, handcrafted local features rely on low-level image information, such as pixel gradients, for keypoint detection and descriptor generation. These features are sensitive to changes in lighting conditions, leading to unstable tracking and potential SLAM failures. Furthermore, monocular SLAM lacks the ability to perceive the depth of keypoints, resulting in ambiguous scale estimation for mapping. To address the limitations of handcrafted feature-based methods, some studies have incorporated deep learning techniques into visual SLAM [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. These approaches, known as learning-based visual SLAM, can be divided into end-to-end learning methods and learning-feature-based SLAM methods. End-to-end learning approaches directly convert images into pose or depth estimates, achieving globally consistent scale estimation by training on large-scale datasets. However, these methods lack additional optimization modules and cannot correct accumulated localization errors. In contrast, learning-feature-based SLAM combines deep neural networks for visual feature extraction with traditional multi-view geometry models in the back-end, offering a hybrid approach of \"geometric model + learning.\" These methods leverage self-supervised local features for scene perception, exhibiting robustness in complex environments with significant lighting variations. However, they may struggle in texture-sparse scenes, where the front-end lacks global feature perception, and monocular keypoints do not provide depth information, leading to scale degradation. Additionally, feature matching in these methods often relies on a constant velocity model, which may not align with real-world motion, further limiting their robustness and performance. In light of these challenges, we propose a novel hybrid visual SLAM system that combines end-to-end visual methods with learning-feature-based approaches. Our system, **D3L-SLAM**, integrates **D**eep keypoints, **D**eep depth estimation, **D**eep pose priors, and a Line detector to enhance the SLAM front-end's performance in complex lighting conditions. By incorporating learned local features, trained in a self-supervised manner, into the tracking module, D3L-SLAM improves feature tracking in challenging environments. To ensure stable performance in texture-sparse scenarios, we introduce line features into the front-end. Additionally, we integrate self-supervised pose estimation as a motion model for adjacent frames, refining the matching of point-line features.