## 2 Related Works
### Hybrid Visual SLAM  
Combining model-based and learning-based SLAM approaches to form a hybrid system leverages the strengths of both methods. GCN-SLAM [11] utilizes a deep learning feature extraction algorithm [22] to generate binary descriptors similar to ORB descriptors, which are directly applicable to SLAM. By deploying GCN on the front-end of the ORB-SLAM2 system and using a single-layer image pyramid for feature extraction, the algorithm improves efficiency. GCN-SLAM also trains a BoW model offline for loop node detection and closure, showing improved performance over ORB-SLAM2 in indoor scenes. DXSLAM [10] incorporates learning-based floating local features from [23] and constructs a loop node recognition module using the fast bag-of-words model FBow [24], which is faster than DBow2 and DBow3. DXSLAM further integrates NetVLAD [25] for global feature extraction and relocalization. It uses NetVLAD to identify candidate loop keyframes similar to the current frame's global feature, then performs local feature extraction and loop node detection using a BoW model built with FBow. LIFT-SLAM [12] uses a learned LEFT extractor for feature extraction and constructs a loop closure module with an offline-trained BoW model. Despite this, experimental results indicate only modest improvements compared to traditional VSLAM methods. SC-Depth [14] proposes a pseudo RGBD VSLAM system, enhancing monocular SLAM by incorporating depth and pose estimation for improved perception and feature tracking. Ref. [26] combines self-supervised depth labels as a pseudo depth sensor and constructs a pseudo stereo SLAM with a virtual baseline to improve localization performance. AirVO [3] uses SuperPoint [27] for feature extraction and stereo images as input, enhancing perception in low-texture environments by also extracting line features. The framework employs Nvidia TensorFlowRT for real-time inference, showing superior performance in low-light conditions compared to model-based visual VO or SLAM. However, AirVO lacks a loop closure module, which limits its ability to correct accumulated errors. SP-Loop [15] converts SuperPoint descriptors into 32-bit integer descriptors through binarization and trains an offline BoW model for loop closure. SL-SLAM [13] combines stereo and IMU data to form a robust feature extraction front-end for scenes with poor lighting. It uses SuperPoint with LightGlue [28] for feature extraction and matching, trains a BoW model based on SuperPoint features using DBow3, and performs loop correction with this BoW model. SL-SLAM demonstrates superior performance across various datasets compared to model-based ORB-SLAM3. Ref. [29] introduces depth estimates generated by the LiDAR depth completion network to form an RGB-L visual SLAM system, benefiting from the precise global absolute scale of LiDAR for accurate pose and depth estimation. D3 VO [4] integrates learned pose and depth priors into the direct method DSO [30], enhancing the front end's feature matching rate and serving as an initial value for depth filtering. However, D3 VO lacks a loop closure module and is unable to perform error correction. These two works [31; 32] also introduce line features in SLAM. Among them, PLDS-SLAM [31] uses semantic segmentation to identify dynamic objects in the scene, and when estimating pose and mapping, it removes the negative impact of dynamic objects.  
A comparison of these methods with the proposed D3L-SLAM is summarized in Table 1. The table highlights how D3L-SLAM integrates deep learning techniques more comprehensively.