# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 4 Experiments
### Ablation Study
In contrast, \"Ours3\" uses a two-stage strategy for stable tracking, combining a semi-direct method for coarse pose estimation and refined feature matching. **(3) The analysis of loop-closing module:** We further perform ablation studies on the proposed loop closure method to assess its performance. Precision-recall metrics on KITTI 00, 05, and 06 sequences are illustrated in Figure 9, with additional evaluation at 100% precision. The quantitative results, presented in Table 5, reveal our deep BoW's superior recall rate compared with traditional BoWs. Notably, in the KITTI 06 sequence, our BoW achieves a recall rate that is remarkably close to 100%. In contrast, the recall rate of the iBoW, belonging to the traditional BoW category, is significantly lower, suggesting that handcrafted descriptors might struggle to accurately identify loop nodes in a sequence. The learned local descriptor captures high-level information, enhancing robustness across diverse environments. This feature stability, unaffected by lighting changes, results in superior loop detection performance. **(4) The analysis of SLAM efficiency:** We conduct an ablation study on the computational efficiency of DK-SLAM, analyzing the time consumption of different modules. We conduct the ablation study on KITTI data. Additionally, we calculate median and mean feature tracking times for each SLAM module. Based on the results, as shown in Table 6, MAML indicates the MAML-based local feature, Two-Stage indicates the coarse-to-fine feature tracking, and Online BoW indicates the online learning for binary BoW. The first row of results corresponds to the official ORB-SLAM2 pipeline without any proposed modules. The results indicate that adding MAML-based features significantly increases the feature tracking time. This is because the learned features are inferred on the GPU, while image processing, feature tracking, back-end optimization, and loop closure are all processed on the CPU. Transferring the inferred features from the GPU to subsequent CPU threads involves data transmission, leading to increased time cost. The coarse-to-fine feature tracking also increases the time consumption, but not significantly. This is because we employ local photometric constraints rather than global constraints, limiting the rise in computational cost. The online BoW increases additional time as well. However, by binarizing the floating-point descriptors to a compressed binary vector, we reduce the computational cost, mitigating the impact of the online BoW on processing time. Additionally, we conduct a comparative analysis of feature tracking time consumption against other typical monocular VSLAMs. As illustrated in Figure 10, handcrafted feature-based ORB-SLAM3 and learned feature-based SuperPoint-SLAM [60] were selected for comparison. Notably, the proposed DK-SLAM demonstrates significantly higher efficiency than SuperPoint-SLAM. This performance advantage stems from fundamental architectural differences: SuperPoint-SLAM is developed using libtorch [61], which transfers images to the GPU individually for processing. In contrast, our method leverages TensorRT's C++ implementation. TensorRT provides hardware acceleration capabilities that pre-cache \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**MAML**} & \multirow{2}{*}{**Two-Stage**} & \multirow{2}{*}{**Online BoW**} & \multicolumn{2}{c}{**Tracking Time (ms)**} \\ \cline{3-5}  & & & **Median** & **Mean** \\ \hline  & & & 38 & 42 \\ ✓ & & & 70 & 76 \\ ✓ & ✓ & & 73 & 78 \\ ✓ & ✓ & ✓ & 75 & 78 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of time consumptions of different modules. Figure 9: Precision–recall curves depicting the performance of the Bag-of-Words (BoW) approach in the proposed DK-SLAM on Sequences 00, 05, and 06 of the KITTI dataset. images in memory and enable parallel processing, thereby achieving efficiency gains over the libtorch-based approach. Nevertheless, DK-SLAM still exhibits an efficiency gap compared with ORB-SLAM3.