## 2 Literature Review  
This section provides a review of the research progress on the visual positioning of intelligent vehicles in underground parking lots, as shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classical Localization in Unknown Environments** & **Localization Process** & **Classification of Methods** & **Disadvantages** \\ \hline \multirow{6}{*}{Visual localization method} & \multirow{2}{*}{Visual map construction} & HD map [29; 30; 31; 32; 33] & Large data volume, high storage requirements, and low matching efficiency \\ \cline{3-4}  & & Lightweight map [26] & Less features, reduced accuracy \\ \cline{2-4}  & & Sparse feature-based matching methods [34; 35; 36; 37; 38; 39; 40] & Insufficient matching accuracy \\ \cline{2-4}  & & Multi-level matching method & Mismatching of feature points in repeated textures \\ \cline{2-4}  & & \"Coarse-to-fine\" matching strategy [43] & High dependence on node density \\ \cline{2-4}  & & Localization method considering trajectory prediction [27; 28; 44] & High dependence on node density \\ \hline \multirow{6}{*}{Visual localization in underground parking lots} & \multirow{2}{*}{Panoramic monitoring for identifying idle parking spaces [45]} & Unable to obtain precise pose of parking spaces and vehicle \\ \cline{2-4}  & & Berth codes matching method [46] & Mismatching caused by encoding occlusion \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the literature review. The visual localization method based on low-cost cameras is a classical approach that enables high-precision vehicle localization in unknown environments. The VO method estimates the current pose of the vehicle by acquiring time-series localization data. However, due to observation errors, using time-series estimation methods can lead to accumulated errors and cause the trajectory of the vehicle to deviate from the actual route over time. V-SLAM methods perform mapping and localization simultaneously, utilizing map-matching techniques to achieve loop closure detection and improve localization accuracy. However, even with V-SLAM, there still exists a significant problem of accumulated errors as the driving distance increases, resulting in a gradual reduction in the localization accuracy. A widely adopted approach is to decouple the localization process into two stages: visual map construction and map-based localization [17]. The current focus of visual map construction is primarily on improving the localization accuracy. The typical approach involves extracting the feature information from the target scene to build an HD map. An HD visual map can provide accurate and stable position references for localization [29], thereby minimizing cumulative errors. For example, [PERSON] et al. proposed a lane-segmentation detection method, named Lane-DeepLab, for detecting multi-class lane lines in unmanned driving scenarios for high-definition maps [30]; [PERSON] et al. extracted semantic information from an HD map and performed data association (DA) to obtain high-precision vehicle poses [31]; [PERSON] et al. exploited a line-detection algorithm to retrieve the required information from camera images, thus constructing a lane-level HD map [32]; and [PERSON] et al. introduced the use of an abundance of visual features and multi-frame HD map landmark features to constrain the estimation of the vehicle position and improve localization accuracy [33]. Although HD maps provide rich information support, they have disadvantages such as a large data volume, high storage requirements, and a low matching efficiency. Therefore, it is necessary to explore new solutions to improve maps' real-time performance, accuracy, and matching efficiency to better meet the requirements of high-precision localization systems. In the map-based localization stage, the first step is to find the map node in the visual map that is most similar to the current image.