# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 5 Experiments
### Comparison with Visual SLAM Approaches  
In this section, we compare the results obtained by our method with those of state-of-the-art visual SLAM approaches, namely, ORB-SLAM2 [3], ORB-SLAM3 [4], OpenVSLAM [28], and UcoSLAM [5]. To validate each method, a map of the environment was built for each video sequence in the dataset. These pre-built maps were then used in a second run in localization mode, and the performance was evaluated in terms of the Absolute Trajectory Error (ATE) and the percentage of correctly tracked frames (Trck). The results are presented in Table 5.  
The results in the comparison table present a clear picture of the trade-offs inherent in purely visual SLAM systems.  
* ORB-SLAM3 suffers from very high trajectory errors in the sequences where it can be initialized, despite maintaining a high tracking rate (100%). This behavior is likely exacerbated by the low camera frame rate of 10 Hz, as feature-based SLAM systems often rely on higher frame rates for more reliable frame-to-frame motion estimation. It also fails to produce results for some datasets.
* ORB-SLAM2, its predecessor, shows a different performance profile. It achieves the best accuracy among all tested SLAM systems on certain datasets, such as HKULandmark (ATE: 0.022 m) and RetailStreet (ATE: 0.032 m). However, it proves to be highly unreliable, with tracking failures on the majority of the other sequences, where its tracking rate often drops below 50%.
* OpenVSLAM performs significantly better than ORB-SLAM3 in terms of accuracy but exhibits variable tracking robustness. It notably fails to track well in more dynamic or large-scale scenes, such as Drive (Trck: 40.7%) and RedSculpture (Trck: 41.7%).
* UcoSLAM exhibits a more balanced performance in some cases but can be unpredictable, failing completely in others (e.g., Trck: 2.8% on RedSculpture).  
These comparisons highlight a key weakness in purely visual SLAM systems: a lack of consistent reliability. Each system excels in certain conditions but is fragile in others. In contrast, our proposed framework, by anchoring its localization to a geometrically stable LiDAR map, achieves comparable or superior performance across a broader range of challenging datasets. This demonstrates that our approach is a valid and highly competitive  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**ORB-SLAM3**} & \multicolumn{2}{c}{**OpenVSLAM**} & \multicolumn{2}{c}{**UcoSLAM**} & \multicolumn{2}{c}{**ORB-SLAM2**} \\  & **ATE (m)** & **Trck (\%)** & **ATE (m)** & **Trck (\%)** & **ATE (m)** & **Trck (\%)** & **ATE (m)** & **Trck (\%)** \\ \hline Garden & 22.875 & 100.0 & 0.209 & 83.8 & 0.017 & 86.9 & 0.073 & 39.7 \\ RetailStreet & - & - & 0.036 & 99.1 & 14.397 & 46.2 & 0.032 & 100.0 \\ CBDBuilding & 31.527 & 100.0 & 0.315 & 100.0 & 0.100 & 100.0 & 0.065 & 48.9 \\ HITGrafifiWall & - & - & 0.075 & 98.0 & 0.089 & 96.6 & 3.517 & 26.7 \\ HKULandmark & 36.124 & 100.0 & 0.030 & 93.7 & 0.039 & 95.3 & 0.022 & 97.8 \\ Drive & 39.113 & 100.0 & 1.297 & 40.7 & 0.485 & 92.1 & 0.033 & 15.8 \\ HKUCulturalCenter & - & - & - & - & - & - & 0.207 & 22.7 \\ RedSculpture & 17.705 & 100.0 & 0.931 & 41.7 & 0.007 & 2.8 & 1.578 & 10.0 \\ SYSU & 6.797 & 100.0 & 0.062 & 100.0 & 0.102 & 100.0 & 0.067 & 99.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative evaluation of ORB-SLAM3, OpenVSLAM, UcoSLAM, and ORB-SLAM2 results on the datasets. The table reports the Absolute Trajectory Error (ATE) and tracking percentage (Trck).  
alternative to traditional visual SLAM, particularly in situations where robustness and consistency are crucial.