## 4 Experiments
### Evaluation with Self-Collected Dataset  
As illustrated in Figure 7, we constructed a car-driving experiment platform equipped with an RGB camera and a high-precision GNSS system to provide ground truth location data for evaluation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Seq**} & \multicolumn{2}{c}{**VISO-M**} & \multicolumn{2}{c}{**LDSO**} & \multicolumn{4}{c}{**ORB-SLAM3**} & \multicolumn{4}{c}{**LIFT-SLAM**} & \multicolumn{4}{c}{**D3L-SLAM**} \\ \cline{2-13}  & \(\mathbf{t_{rel}}\) & \(\mathbf{r_{rel}}\) & \(\mathbf{t_{rel}}\) & \(\mathbf{r_{rel}}\) & \(\mathbf{RMSE}\) & \(\mathbf{t_{rel}}\)  
Our car-driving dataset was collected using RGB images from Zed cameras and positioning data from Sinognss Navigation's GNSS system. The data collection occurred in Changsha, China, covering three distinct scenarios: campus, urban street, and residential areas. The training dataset comprises 57,948 images, encompassing various scene types, while the testing dataset includes 14,972 images, focusing on campus and urban street scenes. Example images from these scenarios are shown in Figure 8. Four sequences were selected for testing: sequences 01, 03, and 04 represent urban data, and sequence 02 represents campus data.  
Given the centimeter-level positioning accuracy of the GNSS system, we assessed the positioning accuracy of the proposed SLAM using root mean square error (RMSE). Since the proposed SLAM does not provide absolute scaled poses, trajectory scaling is necessary before evaluating pose estimates. The RMSE formula is given by  
\[\text{RMSE}=\sqrt{\frac{1}{N}(\mathbf{p}_{i}-\mathbf{p}_{i}^{\text{g}})} \tag{24}\]  
where \(\mathbf{p}i\) denotes the three-dimensional location estimate at the \(ith\) frame, \(N\) is the total number of frames, and \(\mathbf{p}_{i}^{\text{g}}\) represents the ground truth location.  
As shown in Figure 9 and Table 3, the trajectory of the proposed D3L-SLAM closely aligns with the ground truth. In terms of average positioning error, D3L-SLAM demonstrates the best performance. Traditional handcrafted visual SLAM methods often suffer from scale degradation in expansive urban scenes due to the vast distances and sparse textures in such environments. These methods may struggle to accurately reflect camera motion in low-texture areas, leading to scale ambiguity.  
In contrast, the self-supervised monocular depth estimation framework in D3L-SLAM achieves a globally consistent scale for the scene through training, offering relatively accurate relative depth information. This enables the SLAM system to maintain a consistent scale in mapping. The integration of learned pose priors ensures that feature matching between adjacent frames adheres to the true motion model, enhancing localization accu  
Figure 8: Examples of Images from Three Different Scenes in Our Self-Collected Car-Driving Dataset.  
Figure 7: Our Platform Used for Collecting a Large-Scale Car-Driving Dataset for Evaluating Visual SLAM.  
racy. Additionally, incorporating line features improves perception in low-texture areas, mitigating localization degradation.  
However, it is important to note that the positioning accuracy of sequence 02 is not as high as that of traditional ORB-SLAM3. This discrepancy may be attributed to the presence of numerous small-sized dynamic objects, such as people and bicycles, in the campus environment. The instability of line features on these small objects can negatively impact feature tracking performance.