# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 4 Experiments
### Ablation Study  
Table 3 summarizes the results of the ablation study into the key modules within the DK-SLAM system, assessing the impact of our MAML-based deep keypoint meta-learning and coarse-to-fine keypoint tracking. For a fair comparison, \"Ours1\", \"Ours2\", and \"Ours3\" all use a keypoint search radius of 7. **(1) The analysis of keypoint meta-learning module:** The module's effectiveness is evident when comparing \"Ours2\" and \"Ours3.\" \"Ours2,\" which employs the original SuperPoint strategy without our keypoint meta-learning approach, performs slightly worse than \"Ours3,\" which integrates MAML-based keypoint meta-learning. In \"Ours3,\" MAML is applied to train the local feature extractor by iteratively enhancing generalization on support and query sets, resulting in a robust and adaptable detector. The batch size of \"Ours2\" and \"Ours3\" is set as 8. Both models were trained for 150K iterations. In \"Ours3\", we split every training batch to 4 mini-batch support sets and 4 mini-batch query sets. As mentioned in Algorithm 1, the number of update iterations in inner loop is 4. Table 4 presents the average number of matching points for ORB-SLAM3, Ours2, and Ours3 on Sequences 00, 05, 09, and 10 of the KITTI dataset. Setting the total number of keypoints to 3000 and the pyramid level to 3, MAML-based training significantly improves the feature detection performance of SuperPoints, allowing them to acquire more robust features and enhancing SLAM accuracy. The enhanced SLAM performance attributed to the proposed keypoint meta-learning can also be observed in feature matching results, as depicted in Figure 7. Using SuperPoint for matching increases the number of matches, addressing ORB-SLAM3's limitations in texture-less areas. ORB-SLAM3's descriptors, describing lighting changes around corners, are susceptible to environmental variations. In contrast, the original learning-based local feature methods such as SuperPoint capture robust deep features unaffected by lighting changes, ensuring stable tracking across consecutive frames. Furthermore, compared with original Superpoint, MAML-based keypoint is better at capturing inter-frame matching features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Seq** & **ORB-SLAM3** & **Ours2** & **Ours3** \\ \hline K00 & 228.3 & 352.6 & **376.7** \\ K05 & 219.5 & 371.2 & **383.1** \\ K09 & 150.4 & 258.0 & **265.3** \\ K10 & 171.4 & 306.0 & **323.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The average number of matching points for ORB-SLAM3 and DK-SLAM without (Ours2) or with feature meta-learning (Ours3). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Method** & **MAML** & **Two-Stage** & **Metric** & **00** & **02** & **03** & **04** & **05** & **06** & **07** & **08** & **09** & **10** & **Avg** \\ \hline Ours1 & ✓ & & \(t_{rel}\) & 2.88 & - & **4.29** & 1.24 & 2.80 & **7.64** & 1.76 & - & **2.70** & - & - \\ \cline{3-19}  & & & \(r_{rel}\) & 0.41 & - & **0.16** & 0.31 & 0.35 & 0.18 & 0.64 & - & 0.24 & - & - \\ \hline Ours2 & & ✓ & \(t_{rel}\) & 2.92 & 4.42 & 4.54 & 2.01 & 2.48 & 7.97 & 2.11 & 8.85 & 4.03 & 8.48 & 4.78 \\ \cline{3-19}  & & & \(r_{rel}\) & 0.38 & 0.34 & 0.21 & **0.16** & 0.34 & 0.18 & 0.46 & **0.26** & **0.20** & 0.23 & 0.28 \\ \hline Ours3 & ✓ & ✓ & \(t_{rel}\) & **2.57** & **4.38** & 4.70 & **1.12** & **1.98** & 8.20 & **1.11** & **7.61** & 2.97 & **7.03** & **4.17** \\ \cline{3-19}  & & & \(r_{rel}\) & **0.31** & **0.27** & 0.17 & 0.24 & **0.26** & **0.17** & **0.28** & 0.28 & 0.29 & **0.23** & **0.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study into the key modules in our DK-SLAM system. **(2) The analysis of coarse-to-fine tracking module:** In \"Ours1\", lacking a coarse-to-fine tracking strategy, reliance on a constant velocity motion model for keypoint matching leads to errors, resulting in inaccurate correspondence and tracking failure. Figure 8 shows a failure case for \"Ours1\" in accurate feature matching due to uniform motion model challenges.