## 5 Conclusions  
The core technology of navigation for unmanned platforms and autonomous driving systems is SLAM, and visual SLAM is widely used due to its low cost advantage. However, monocular visual SLAM often struggles with accurate positioning and mapping in large-scale scenes with low texture due to a lack of local features. Although loop closure methods can correct mapping scale, accurate localization remains challenging in large-scale environments without sufficient loop nodes. This paper proposes a hybrid monocular SLAM system that integrates a self-supervised ego motion estimator with a multi-view geometry model. Our approach combines self-supervised depth and pose estimates. The depth estimates assign depth values to each keypoint, enabling the front end to perceive the relative distances between objects. We achieve this by designing a virtual baseline that transforms the pseudo-RGBD front end into a virtual stereo front end. Additionally, we replace the uniform motion model in the tracking thread with pose priors to better reflect the real motion model, enhancing feature matching accuracy and, consequently, localization precision. To further improve SLAM performance in low-texture environments, we developed a hybrid line-point front end and added more constraints in the optimization back end. The experiments conducted on both publicly available and self-collected datasets, compared to typical benchmarks, demonstrate that our proposed D3L-SLAM significantly improves localization accuracy. However, we observed a decrease in rotation accuracy, likely due to the fact that the learned depth estimates are not actual depth measurements, which can introduce ambiguity and increase rotation estimation errors. Despite this, the rotation accuracy of D3L-SLAM remains comparable to that of traditional SLAM systems.  
The future work will focus on enhancing rotation estimation accuracy by incorporating IMU data to create a virtual \"stereo+IMU\" SLAM. We also plan to introduce stereo data during the training of the self-supervised ego motion estimator to optimize scale estimation, enabling the SLAM framework to obtain absolute scale poses. In addition, we also plan to apply the proposed SLAM to the unmanned aerial vehicles and autonomous vehicles, to achieve accurate positioning in complex lighting environments.  
Conceptualization, H.Q. and C.C.; methodology, H.Q. and C.C.; software, H.Q.; validation, H.Q., Y.X. and C.W.; formal analysis, H.Q.; investigation, L.Z.; resources, X.H.; writing--original draft preparation, H.Q. and C.C.; writing--review and editing, H.Q. and C.C.; visualization, C.W.; supervision, X.H.; project administration, X.H. and C.C.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Comparison of tracking time of ORB-SLAM3, D3L-SLAM, and Superpoint-SLAM.  
**Funding:** This work was supported by National Natural Science Foundation of China (NFSC) under the Grant Number of 62103427, 62073331, 62103430, and Major Project of Natural Science Foundation of Hunan Province (No. 2021 JC0004). [PERSON] is funded by Young Elite Scientist Sponsorship Program by CAST (No. YESS20220181)  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.