# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 2 Related Work  
### Feature-Based Localization  
In the field of feature-based localization for vehicle positioning and orientation estimation relative to a map, lane markings have been recognized as crucial features for autonomous localization in urban and highway environments. The lane-match method in [5] used a hybrid particle filter and factor graph to address ambiguities, demonstrating robustness on well-marked roads. Detected lane markings coupled with map data were used for ego-localization [6]. Furthermore, lane edges, including solid and dashed lane boundaries, are extracted from images and point clouds for cross-modality registration [7], where dashed lines compensate for inadequacies commonly found on highways. However, these methods heavily rely on the presence of lane markings, making them less reliable in environments with fewer or less visible lane markings.  
To mitigate these limitations, several studies have integrated lane markings and curbs from both camera images and HD maps to achieve precise and robust localization [8]. Various features, including pole-like objects, traffic signs, and lane lines, were detected to match the light-weight HD map [9]. Lane markings, lane borders, curbs, traffic lights, and traffic signs are recognized from images [10] and implicitly associated with the HD map by applying a distance transform on binary images of each semantic class of interest.  
However, in scenarios such as long, straight roads or intersections lacking distinct visual cues, positioning errors can still escalate due to limited feature availability. An accurate position estimation [11] was provided at intersections with ambiguous landmarks through maximum a posteriori determination. Lane markings, delimiters, and stop lines were extracted as the key information for intersection localization in [12]. Road markers, such as lane markers, stop lines, and traffic sign markers from a digital map, improved localization accuracy in [13]. Traffic lights were used to reduce localization errors in [14].  
The studies listed in Table 1 provide a quantitative comparison of feature-based localization methods, highlighting their respective scenarios, accuracy, and key challenges. From the literature, there is a clear lack of emphasis on determining the relative importance of different feature types for registration accuracy. Most methods treat features equally without evaluating their individual contributions to pose estimation. This gap limits our understanding of which features are critical under different circumstances and how their availability affects robust localization. Our proposed approach seeks to overcome these limitations by enhancing robustness across diverse environments through a more comprehensive integration of multiple feature types and a deeper understanding of their contributions to localization accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Features Used** & **Scenario** & **Average Localization Error** & **Key Challenges** \\ \hline
[5] & Lane markings & Well-marked roads & Lateral: 0.066 m, Longitudinal: 0.078 m & Over-reliance on lane markings \\ \hline
[6] & Lane markings & Urban/highway roads & Lateral: less than 0.10 m & Over-reliance on lane markings \\ \hline
[7] & Lane edges (solid, dashed) & Highway & Rotation: 0.3\({}^{\circ}\), Translation: 0.05 m & Over-reliance on lane markings \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Feature-Based Localization Methods for Autonomous Driving.