# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 4 Experiments
### Sequential Experiment  
The Seqential Experiment aimed to assess the method's performance along a predefined trajectory, encompassing various environmental conditions, viewpoints, and image recognition scenarios. It provided insights into the consistency, accuracy, and robustness of the methods in real-world scenarios. Two sequential trajectories from the Argoverse dataset were used for the experiments. Figure 12 shows the qualitative results using the proposed EOR method, employing the projection of HD map objects onto images for quantitative analysis. The displayed frames represent different stages: approaching an intersection, inside the intersection, and leaving the intersection. In the frames approaching an intersection, where lane markings and poles are abundant, we observed relatively good and steady registration results. However, frames captured inside the intersections, where only poles and some signs were available for registration, introduced challenges. Figure 13 illustrates the sequential SDF values alongside the IoU values. The upper blue part of the figure represents the SDF values, including occupancy 3D, occupancy \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Mean Translation Errors**} & \multicolumn{3}{c}{**Mean Rotation Errors**} & \multirow{2}{*}{**IoU**} & \multirow{2}{*}{**P (IoU \textgreater{} 0.2)**} \\ \cline{2-2} \cline{5-8}  & **x (m)** & **y (m)** & **z (m)** & & **Pitch (rad)** & & **Yaw (rad)** & **Roll (rad)** \\ \hline SampleInit & 0.48 & 0.44 & 0.46 & 0.05 & 0.05 & 0.05 & 0.04 & 1\% \\ \hline EOR & 0.18 & 0.21 & 1.48 & 0.01 & 0.01 & 0.02 & 0.28 & 90\% \\ \hline MOR & 0.20 & 0.24 & 1.29 & 0.01 & 0.01 & 0.03 & 0.28 & 87\% \\ \hline VisionHD & 0.14 & 0.24 & 2.47 & 0.01 & 0.01 & 0.03 & 0.24 & 65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Random Experiment Errors and IoU. Figure 12: Qualitative Results of EOR method. The columns are approaching, inside and leaving intersection respectively. (**a**) Sequence 1; (**b**) Sequence 2. 2D, GDOP 3D, GDOP 2D, and the longitudinal difference of respective object categories. While the lower white part shows the registered IoU values. The red shaded regions indicate intersection areas. From Figure 13, we observe that the occupancy of lane markings decreases inside the intersections, while the occupancy of poles increases. Traffic signs tend to appear at a distance along the road. However, lane markings' occupancy does not always decrease to zero within the intersection area, as different crossroads and T-junctions may have various patterns of painted lane markings. The occupancy 3D and occupancy 2D, as well as GDOP 3D and GDOP 2D, exhibit similar trends but not always identical. Table 5 provides a quantitative comparison of the mean translation errors, rotation errors, and IoU values across two sequences. For Sequence 1, the EOR and MOR methods have similar and higher mean IoU (0.32 and 0.33, respectively), compared to VisionHD (0.29). This suggests that both EOR and MOR methods produce similar overall registration results in Sequence 1. However, EOR exhibits significantly lower errors in the y and z directions (0.19 m and 0.94 m) compared to MOR (0.49 m, 1.46 m) and VisionHD (0.19 m, 1.45 m). This highlights the robustness of EOR, particularly in longitudinal (z) control.