# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 4 Experiments
### Pose Evaluation in the Car-Driving Scenario  
To evaluate the effectiveness of our SLAM system in the car-driving scenario, we conducted experiments using the KITTI dataset and employed the official evaluation metrics, computing root-mean-square error (RMSE) for both translation and rotation vectors. This evaluation spanned sequences ranging from 100 m to 800 m, providing an overall metric for pose accuracy. We conducted a comprehensive assessment of our SLAM system's tracking and loop closure capabilities in complex environments, focusing on Sequences 00, 02, 03, 04, 05, 06, 07, 08, 09, and 10 from the KITTI dataset. We excluded Sequence 01 from our evaluation due to its unique challenges for monocular SLAM systems, as a highway scene with few distinctive feature points, especially near the horizon, led to significant scale degradation in monocular SLAM performance. We benchmarked our DK-SLAM against several leading SLAM systems: LDSO [52], ORB-SLAM3 [4], VISO-M [53], and LIFT-SLAM [11]. The comparison of trajectories generated by the proposed SLAM and benchmarks is shown in Figure 4. Among these, VISO-M exhibited the least accuracy, particularly struggling in low-light conditions where its corner detection algorithms were unstable. LDSO, which includes a loop closure module, managed to reduce accumulated errors effectively in challenging scenarios. ORB-SLAM3, a sophisticated keypoint-based SLAM system that uses FAST corner detection and BRIEF descriptors, performed exceptionally well across most sequences, as shown in Table 1.  
However, it showed a noticeable drop in performance on Sequence 10, primarily due to a scarcity of reliable corner points, which resulted in unstable tracking and adversely affected overall pose estimation accuracy. The reliance on BRIEF, which describes local photometric information around corners, made ORB-SLAM3 particularly sensitive to lighting changes, thereby impacting feature tracking performance. In environments with significant lighting variations, the inability to track reliable feature points at the front-end increased positioning errors. Although the back-end loop closure could correct some accumulated errors, it struggled to improve overall accuracy. In contrast, LIFT-SLAM, which depends on learning-based local features, generally underperformed across most sequences. This outcome suggests that while learning-based features have potential, they do not consistently outperform traditional feature-based methods. ORB-SLAM3's attention to scale, direction, and a traditional SLAM system for achieving an even keypoint distribution gave it a competitive advantage, helping to filter out mismatched features at the front-end. Our DK-SLAM system demonstrated exceptional performance across most KITTI sequences. Specifically, DK-SLAM shows a substantial improvement over traditional monocular ORB-SLAM3, achieving approximately 17.7% better translation accuracy and 24.2% higher rotation accuracy on the KITTI dataset. When compared with LIFT-SLAM, a leading SLAM system based on learned features, DK-SLAM surpasses it by nearly 2.7 times in translation accuracy and 9 times in rotation accuracy. This significant enhancement in performance is attributed to DK-SLAM's use of meta-learning-based feature extraction and a coarse-to-fine matching strategy, which focuses on keypoint-surrounding patches and optimizes pose estimation through precise 3D-2D matching relationships. Additionally, our system's online training capability builds a learning-based Bag-of-Words (BoW) model using previously acquired data, efficiently constraining the BoW's feature description space and improving loop scene detection. This approach enables the loop closure module to accurately correct cumulative errors. Moreover, as illustrated in Figure 5, DK-SLAM excels in mapping performance, providing detailed geometric insights. The incorporation  
Figure 4: The generated trajectories of our proposed DK-SLAM on Sequences 00, 02, 05, 07, 09, and 10 of the KITTI dataset, compared with LDSO and ORB-SLAM3.