## 4 Experiments
### Evaluation on the KITTI Odometry Dataset  
We first conducted experiments using the publicly available KITTI dataset, which is a standard benchmark to evaluate visual SLAM for autonomous vehicles scenarios. For the proposed hybrid visual SLAM, we trained the self-supervised odometry system on KITTI odometry 11-20 sequences. We also evaluated the localization performance of the proposed SLAM and baselines on sequences 0-10. The traditional visual baselines (VISO-M, LDSO and ORB-SLAM3) do not include the training process. Moreover, LIFT-SLAM uses the COCO dataset [37] to supervise the training of LIFT extractor. We also use the COCO dataset to supervise the training of the proposed deep keypoint. We set the maximum number of keypoints to 3000 and the number of layers in the image pyramid to 3. Due to the scarcity of local features, many monocular SLAM systems struggle with sequence 01. Consequently, we did not assess the performance of our SLAM method on this sequence to ensure fairness. We calculated the mean square error of translation and rotation estimates on different length sequences \([100,200,\dots,800]\) and took their mean as the mean square error of translation and rotation estimates. These two mean square errors are used as accuracy indicators for SLAM localization metric \(t_{rel}\) and rotation metric \(r_{rel}\). The specific calculation of these two metrics is shown in Equation (23), where \(i\) and \(j\) represent the index of two frames separated by a fixed distance. \(len(i,j)\) represents the fixed distance, which is \([100,200,\dots,800]\). Rot\((. )\) and \(\text{Trans}(. )\) represent the rotation and translation difference. \(\ominus\) means calculating the relative pose estimate between two frames. Additionally, \(\xi_{j}\ominus\xi_{i}\) represents the relative pose from the \(i_{th}\) frame to the \(j_{i}h\) frame. \(\xi\)and \(\xi\) represent the estimated and ground truth pose, respectively. \(N\) indicates the sum of fixed distances. \[ \begin{split} r_{rel}&=\frac{1}{N}\sum_{(i,j)\in \mathcal{S}}\frac{\text{Rot}\Big{(}\Big{(}\xi_{j}\ominus\xi_{i}\Big{)}\ominus \Big{(}\xi_{j}\ominus\xi_{i}\Big{)}\Big{)}}{\text{len}(i,j)}\\ t_{rel}&=\frac{1}{N}\sum_{(i,j)\in\mathcal{S}} \frac{\text{Trans}\Big{(}\Big{(}\xi_{j}\ominus\xi_{i}\Big{)}\ominus\Big{(} \xi_{j}\ominus\xi_{i}\Big{)}\Big{)}}{\text{len}(i,j)}\end{split}  \tag{23}\]  
To thoroughly evaluate the performance of the proposed SLAM, we compared it against several benchmark methods, including traditional feature-based visual SLAM approaches such as VISO-M [40; 41], ORB-SLAM3 [42], and the direct method LDSO [43], as well as the learning-based method LIFT-SLAM [12]. VISO-M RMSE is not shown in Table 2, because its absolute error in KITTI was not indicated in the original paper [40; 41]. The results demonstrate that incorporating learned depth estimates significantly improves SLAM localization performance. As shown in Figure 5 and Table 2, sequence 08, which lacks loop nodes and is demanding in terms of feature tracking and scale degradation suppression, highlights the effectiveness of our approach. Our method achieved superior localization accuracy and closely matched the ground truth trajectory. Monocular SLAM systems often struggle to accurately estimate the scale in long-distance open-loop sequences. In low-texture areas, the front-end of monocular SLAM may only detect keypoints at the distant skyline, leading to the incorrect assumption that the camera is stationary, which results in scale degradation. Our proposed SLAM method mitigates this issue by incorporating DepthNet's depth estimates. Each valid keypoint is assigned a depth estimate, and we use the pseudo-baseline and keypoint depth to compute the corresponding virtual right-view keypoint, effectively converting the monocular SLAM into a stereo SLAM. This approach enhances scale awareness and prevents scale degradation caused by low-texture areas.