# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 5 Experiments
### Overall Performance Comparison  
To provide a conclusive performance benchmark, this section compares our proposed framework against state-of-the-art visual SLAM systems. The evaluation focuses on robust tracking, where a system must not only be accurate but also operate consistently. Therefore, for this head-to-head comparison, we only consider results from systems that successfully track more than 75% of the frames in a given sequence. A \"-\" in the table indicates that the tracking rate for that system fell below this reliability threshold. For our methods, all results are reported using the superior AKAZE detector. The comprehensive results, summarized in Table 6, reveal the strengths and weaknesses of each approach.  
Our proposed framework demonstrates an advantage in the most challenging scenarios. Across the nine datasets, our methods achieve the lowest ATE in five cases when compared to reliable visual SLAM systems.  
The robust comparison is particularly insightful in challenging sequences, such as _Drive_ and _RedSculpture_. On these datasets, most visual SLAM systems fail to maintain tracking and are thus excluded from this analysis. The few that remain operational, such as UcoSLAM on _Drive_ or ORB-SLAM3 on _RedSculpture_, produce substantial errors. In stark contrast, our Offline (M2 Mapping) method provides a significant improvement in accuracy while maintaining high tracking rates, achieving an ATE of just 0.056 m on _Drive_ and 0.103 m on _RedSculpture_.  
Furthermore, on the _HKU_ culturalCenter_ sequence, no competing visual SLAM system could meet the 75% tracking threshold, leaving our framework as the only effective and reliable solution presented. Our Offline (M2 Mapping) approach successfully tracked this sequence with an ATE of 0.063m. In other datasets, such as _CBDBuilding_ and _HITGraffitiWall_, our methods also deliver top performance.  
While highly optimized visual SLAM systems like UcoSLAM, ORB-SLAM2, and Open-VSLAM achieve the best results in some of the more conventional sequences (_Garden_, _RetailStreet_, _HKUlandmark_, and _SYSU_), they lack the consistent reliability of our approach. This analysis confirms the central thesis of our work: by leveraging the geometric stability of a pre-built LiDAR map, our framework provides a more robust and consistently accurate localization solution than state-of-the-art visual-only methods.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Our Methods (AKAZE)**} & \multicolumn{4}{c}{**Visual SLAM Methods**} \\ \cline{2-9}  & \multirow{2}{*}{**Online**} & \begin{tabular}{c} **Offline** \\ **(Point-** \\ **Based)** \\ \end{tabular} & \
\begin{tabular}{c} **Offline** \\ **(M2 Mapping)** \\ \end{tabular} &
\begin{tabular}{c} **ORB-** \\ **SLAM3** \\ \end{tabular} & \multirow{2}{*}{**OpenVSLAM**} & \multirow{2}{*}{**UcoSLAM**} & \multirow{2}{*}{**ORB-** **SLAM2**} \\ \hline Garden & 0.031 & 0.040 & 0.039 & 22.875 & 0.209 & **0.017** & - \\ RetailStreet & 0.037 & 0.037 & 0.037 & - & 0.036 & - & **0.032** \\ CBDBuilding & **0.048** & 0.056 & 0.054 & 31.527 & 0.315 & 0.100 & - \\ HITGraffitiWall & 0.050 & 0.035 & **0.025** & - & 0.075 & 0.089 & - \\ HKULandmark & 0.055 & 0.056 & 0.035 & 36.124 & 0.030 & 0.039 & **0.022** \\ Drive & 0.070 & 0.088 & **0.056** & 39.113 & - & 0.485 & - \\ HKUCulturalCenter & 0.086 & 0.076 & **0.063** & - & - & - & - \\ RedSculpture & 0.107 & 0.108 & **0.103** & 17.705 & - & - & - \\ SYSU & 0.103 & 0.094 & 0.086 & 6.797 & **0.062** & 0.102 & 0.067 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overall ATE (m) performance comparison focusing on robust trackers (Trck > 75%). All results from our methods use the AKAZE detector. A ‘-’ indicates the tracking rate was below 75%. The best result for each dataset is shown in bold.