# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 2 Related Work
### Map Quality Evaluation  
Map quality evaluation is a crucial component for HD maps, with dimensions such as resolution, accuracy, completeness, and consistency previously outlined [15]. These evaluations, however, remain largely focused on individual map attributes without considering the complexities of integrating maps with sensor data from different modalities.  
A framework for evaluating map quality in visual landmark-based mapping [1] used a convex hull approach to determine if query poses lie within regions observed by landmarks, providing a measure of spatial coverage. COLA and OSPA metrics [16] evaluate feature map quality in Simultaneous Localization and Mapping (SLAM). Normal distribution (ND) map quality is assessed through field testing [17]. Offline experiments estimate the uncertainty of 3D normal distribution transform (NDT) scan matching under varying environmental conditions. Criteria for evaluating ND maps for autonomous vehicle localization were introduced in [18], covering feature efficiency, layout, local similarity, and representation quality. Later, [19] extended this work to provide a more detailed modeling of the local similarity criterion, further refining the evaluation process.  
These criteria are defined independently of specific map formats and can theoretically apply to various map types. However, their quantification primarily focuses on the ND map format, limiting practical applicability to other data modalities. These methods provide a quantitative judgment of the map representation abilities but are generally limited to single-modality maps, lacking an evaluation across multiple sensor modalities.  
These existing evaluation methods, as shown in Table 2, do not fully account for the complex nature of data integration needed in cross-modality systems. Evaluating the quality of HD maps when combined with visual sensor data requires considering shared features, something missing in the existing body of literature.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Features Used** & **Scenario** & **Average Localization Error** & **Key Challenges** \\ \hline
[8] & Lane markings, curbs & Urban roads & Positioning: 0.19 m & Limited performance on roads or intersections lacking distinct features \\ \hline
[9] & Pole-like objects, traffic & Urban roads & Localization: 0.24 m & Feature matching error \\ \hline
[10] & Lane markings, lane borders, & & & \\ curbs, traffic lights, and traffic signs & Urban roads & Lateral: 0.19 m, & Errors led by misclassification \\ \hline
[11] & Lane markings, ambiguous & Intersections & Lateral: 0.03 m, & Ambiguity with limited feature availability \\ \hline
[12] & Lane markings, delimiters, stop lines & Intersections & Lateral: 0.32 m, & Relies on stereo cameras \\ \hline
[13] & Lane markers, stop lines, traffic sign markers & Urban roads, intersections & Lateral: 0.076 m, & \\  & & Longitudinal: 0.53 m & Requires multiple computing threads \\ \hline
[14] & Traffic lights & Intersections & Lateral: 0.5 m, & Effective only at intersections \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Evaluation Method** & **Dimensions Assessed** & **Limitations** \\ \hline
[15] & Quality Dimensions & Resolution, Accuracy & No integration with sensor data \\ \hline
[1] & Convex Hull-based Evaluation & Spatial Coverage, Visual Landmark Quality & Focused on visual landmark-based mapping \\ \hline
[16] & COLA \& OSPA Metrics & Feature Map Quality & Focused on SLAM only \\ \hline
[17] & ND Map Field Testing & 3D NDT Scan Matching, Uncertainty Assessment & Limited to pre-experiments \\ \hline
[18; 19] & ND Map Criteria Evaluation & Feature Efficiency, Layout, Local Similarity, Representation Quality & Quantification primarily focused on the ND map format \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation Methods for Map Quality.