# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 1 Introduction  
The precise alignment of visual information from cameras with high-definition (HD) maps is crucial for camera pose estimation in autonomous driving, as depicted in Figure 1. While HD maps provide a detailed and stable global reference frame, aligning them with real-time visual data from cameras presents significant challenges due to the cross-modality differences in data representation and perspective. Overcoming these challenges requires innovative approaches to achieve robust object registration. Existing methods typically process input data without fully understanding its influence on localization performance. These approaches tend to focus on generating output directly, neglecting a thorough analysis of how different features in the scene contribute to registration accuracy. As a result, the varying importance of different object types and their impact on the final pose estimation are often overlooked. This limitation restricts the ability of these methods to adapt to diverse environmental conditions effectively. A key challenge in image-to-HD map alignment lies in identifying the objects that significantly impact registration accuracy and prioritizing their importance. Among theseobjects, lane markings hold particular importance due to their strong geometric constraints. To address this, we introduce two key concepts: spatial distribution factors (SDFs) and spatial structural characteristics (SSC). SDF describes the spatial arrangement of objects in a scene, offering insights into the conditions that promote accurate and robust localization. SSC captures inherent geometric relationships and perspective transformations in image-HD map alignment, addressing challenges such as feature mismatches and variations in object scale. These concepts enhance the understanding of input data, forming the basis for improving registration accuracy and robustness. The concept of SDF is inspired by its application in point cloud-to-point cloud map localization [1], where it predicts localization accuracy based on spatial characteristics of features. In this work, we extend and adapt this idea to the image-HD map alignment context, addressing the unique challenges of cross-modality registration. By combining SDF with SSC, we aim to enhance localization performance and improve robustness in challenging scenarios. However, several challenges must be addressed, including identifying the feature types and scenarios that contribute to optimal localization, ensuring a uniform distribution of features for sensor data matching, and considering the positioning layer in HD maps [2,3]. Complex scenarios, such as at intersections where lane borders rely on human behavior, exacerbate these challenges, along with potential false negative visual detections, outdated map data, and abstract map representations designed to simplify geographic information. By tackling these challenges, our research develops an enhanced object registration (EOR) that leverages SDF and SSC. The method aligns cameras with HD maps using object features such as lane markings, poles, and traffic signs. It quantifies the impact of feature distribution and refines geometric alignment during registration through carefully designed metrics that evaluate the spatial arrangement and positional accuracy of objects. The effectiveness of the EOR is validated through experiments on the open-source Argoverse 2 dataset [4]. The main contributions of this paper can be summarized as follows:  
* SDF: Introduced to analyze the data distribution of objects in cross-modality visual images and HD map registration. These factors are designed to capture the varying importance of different object types and their contribution to localization accuracy and robustness. * SSC: Proposed to enforce constraints during the optimization process, limiting abnormal solutions by leveraging inherent spatial relationships. These characteristics ensure geometric consistency and enhance alignment accuracy.