## 4 Experiments
### Ablation Study  
To validate the contribution of each component in D3L-SLAM, we perform an ablation study. The results are summarized in Table 4. In the table, \"DK\" refers to deep keypoints, which utilize learned local features as the SLAM front-end. \"Depth\" denotes the use of learned depth estimates as a pseudo depth sensor. \"Pose\" signifies the incorporation of pose priors into the front end. \"Line\" indicates the addition of line features at the front end and line segment errors at the back end. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **01 Seq** & **02 Seq** & **03 Seq** & **04 Seq** & **Avg** \\ \hline ORB-SLAM3 & 22.30 & **8.43** & 13.42 & 25.50 & 17.41 \\ LDSO & 70.36 & 117.10 & 53.96 & 24.23 & 66.41 \\ D3L-SLAM & **8.72** & 9.42 & **11.09** & **10.21** & **9.86** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison Of Pose Estimation Results Between The Proposed D3L-SLAM and Representative Visual SLAM Baselines (i.e., ORB-SLAM3 and LDSO). Figure 9: Comparison of Trajectories Generated by the Proposed D3L-SLAM and Representative Visual SLAM Baselines (i.e. ORB-SLAM3 and LDSO). From the results labeled as Ours1, it is evident that deep keypoints substantially enhance both localization and rotation accuracy compared to the handcrafted feature-based ORB-SLAM. This improvement suggests that learned local features are more effective in handling scenes with limited texture. Comparing Ours1 and Ours2, it is clear that learned depth estimates significantly boost SLAM's localization accuracy. These globally consistent depth estimates prevent scale degradation in mapping, even in low-texture areas, by providing relative depth information for each keypoint. However, the rotation accuracy shows a decrease, which may be attributed to inaccuracies in depth estimation for fine details such as edges, affecting rotation estimates. From Ours2 and Ours3, it can be observed that incorporating pose priors enhances the feature matching rate between adjacent frames. This improvement in matching accuracy results from the learned pose priors better aligning with the real motion model, as opposed to the uniform models used in traditional visual SLAM. Finally, comparing Ours3 and Ours4 demonstrates that integrating line features enhances SLAM performance, likely due to the robustness of line features in low-texture environments. In addition to performance comparisons, we also test the efficiency of our proposed SLAM. The experiment baselines are the handcrafted traditional SLAM: ORB-SLAM3 and SuperPoint-SLAM [44], which is similar to our proposed SLAM. To ensure fairness, all these methods are set with a pyramid of 3 levels and a total number of keypoints set to 3000. SuperPoint-SLAM uses libtorch [45] to combine superposition with ORB-SLAM2, while our framework is based on the C++ version TensorRT. The test data used are KITTI, and we measure the median and mean feature tracking time of the three methods. From the bar chart Figure 10, it can be seen that the efficiency of the traditional handcrafted method ORB-SLAM3 is the highest, which is because traditional SLAM only needs to run on CPU and RAM, and ORB is a low-level image feature. Therefore the ORB-SLAM3 is more efficient. Our proposed method uses various deep learning models running on the GPU: DepthNet, PoseNet, and Deep Keypoint, thus it is less efficient than traditional methods. However, we use the C++ version TensorRT to deploy the learning models. In contrast, SuperPoint-SLAM runs on libtorch. It converts each single image to a tensor on the GPU, and then it inferences an image through the learned model. Libtorch transfers the output keypoints and descriptors from the GPU to the CPU for subsequent processing. TensorRT, on the other hand, caches multiple images on the GPU before inference. This caching mechanism can greatly reduce the data transfer time from GPU to CPU, enhancing the efficiency of learning-based SLAM.