# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 4 Experiments
### Pose Evaluation in the UAV Scenario  
As shown in Table 2 and Figure 6, we come to validate our proposed DK-SLAM on the EuRoC dataset. Following the previous research, we address the scale ambiguity with Umeyama alignment [54] for monocular vision methods. Comparisons include ORB-SLAM3 [4], DSO [1], DSM [55], SVO [2], and LIFE-SLAM [11]. Results are derived from [56] for ORB-SLAM3, DSO, DSM, and SVO and [11] for LIFE-SLAM. We utilize the absolute translation error as the evaluation metric. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**Method** & **Sensor** & **Metric** & **00** & **02** & **03** & **04** & **05** & **06** & **07** & **08** & **09** & **10** & **Avg** \\ \hline \multirow{2}{*}{VISO-M} & \multirow{2}{*}{Mono} & \(t_{rel}\) & 36.95 & 21.98 & 16.14 & 2.61 & 17.20 & 7.91 & 20.00 & 39.78 & 29.01 & 28.52 & 22.01 \\ \cline{3-14}  & & \(r_{rel}\) & 2.42 & 1.22 & 2.67 & 1.53 & 3.52 & 1.83 & 5.30 & 1.99 & 1.32 & 3.23 & 2.50 \\ \hline \multirow{2}{*}{LDSO} & \multirow{2}{*}{Mono} & \(t_{rel}\) & 2.84 & 4.91 & 2.75 & 1.58 & 2.07 & **6.49** & 12.76 & 30.17 & 23.14 & 9.20 & 9.59 \\ \cline{3-14}  & & \(r_{rel}\) & 0.36 & 0.74 & **0.16** & **0.19** & **0.24** & **0.16** & 5.13 & 0.24 & **0.21** & **0.21** & 0.76 \\ \hline \multirow{2}{*}{ORB-SLAM3} & \multirow{2}{*}{Mono} & \(t_{rel}\) & 2.80 & 6.04 & 2.71 & 2.22 & 3.41 & 7.07 & 2.14 & 10.35 & 3.13 & 10.78 & 5.07 \\ \cline{3-14}  & & \(r_{rel}\) & **0.3** & 0.38 & 0.17 & 0.24 & 0.39 & 0.18 & 0.42 & 0.31 & 0.54 & 0.36 & 0.33 \\ \hline \multirow{2}{*}{LIFT-SLAM} & \multirow{2}{*}{Mono} & \(t_{rel}\) & 3.18 & 8.73 & **1.46** & 2.22 & 6.09 & 12.24 & 2.42 & 47.10 & 19.91 & 9.72 & 11.31 \\ \cline{3-14}  & & \(r_{rel}\) & 2.99 & 2.49 & 0.34 & 0.48 & 3.11 & 2.91 & 4.02 & 2.02 & 2.14 & 2.24 & 2.27 \\ \hline \multirow{2}{*}{DK-SLAM (Ours)} & \multirow{2}{*}{Mono} & \(t_{rel}\) & **2.57** & 4.38 & 4.70 & **1.12** & **1.98** & 8.20 & **1.11** & **7.61** & **2.97** & **7.03** & **4.17** \\ \cline{3-14}  & & \(r_{rel}\) & 0.31 & **0.27** & 0.17 & 0.24 & 0.26 & 0.17 & 0.28 & 0.28 & 0.29 & 0.23 & **0.25** \\ \hline \end{tabular}
\end{table} 
Table 1: The pose evaluation on the KITTI dataset: our DK-SLAM system outperforms both representative traditional and learning-based monocular SLAM methods. Sequence 01 is excluded from the evaluation due to its inherent challenges for all monocular SLAM baselines, which prevent the generation of reasonable comparative results. Figure 5: Mapping results generated by our proposed DK-SLAM system. The evaluation results reveal that the learned feature-based SLAM, i.e., LIFE-SLAM, often fails in drone scenarios, as it relies on LIFT as a binary feature extractor and uses the Hamming distance between keypoints in consecutive frames for matching. In addition, LIFE-SLAM does not consider the structural relationships between keypoints, leading to a significant number of mismatches and subsequent feature tracking failures. ORB-SLAM3 [4] employs an offline-trained Bag-of-Words (DBow3) for loop closure. Due to the sensitivity of its handcrafted descriptors to lighting variations, ORB-SLAM3 struggles with lower loop detection accuracy in complex lighting conditions, which diminishes the overall performance of the system. In contrast, our DK-SLAM demonstrates outstanding localization performance. It uses a two-stage tracking strategy that precisely locates feature points and minimizes incorrect matches. The learned features in DK-SLAM are robust, performing well even in low-light conditions and supporting stable feature tracking. Moreover, the online learning-based deep Bag-of-Words model excels in loop detection. This is particularly evident in the MH02 sequence, where DK-SLAM surpasses ORB-SLAM3. This superior performance is due to the use of learned features for constructing the Bag-of-Words model, which provides detailed scene differentiation, and the online training capability, which allows for rapid adaptation to changing environments.