## 2 Related Works
However, its effectiveness is limited to two standard parking scenarios--perpendicular and parallel--due to its dependency on standard storage space as a prior condition, thus limiting its generalization performance. [PERSON] et al. [21] proposed a parking line extraction method with main orientation constraints. While this method is not restricted by the type of storage space and only relates to vehicle orientation, it is more suitable for cases where a global map is constructed. The incomplete obstacle information in a local map reduces the accuracy of vehicle orientation assessment, affecting the robustness of parking space detection. Moreover, the aforementioned vision-Lidar fusion methods rely on parking lines in image vision as prior information for detecting parking spaces. This reliance on basic prior knowledge provides a foundation for determining parking spaces. However, in some parking scenarios, clear parking space information is not provided. For instance, the modern ecological parking lot shown in Figure 3 integrates ecological concepts, making the parking lines less obvious. Consequently, vision-based or vision-fusion methods that depend on parking lines as prior information are not applicable in such scenarios. However, the above methods cannot solve the problem of high-precision scene parking detection in unknown environments. In an unknown scene lacking prior parking coordinates and visual texture information, accurately locating an autonomous vehicle and identifying parking spaces relies on the vehicle's onboard detector to perceive the scene environment. This challenge is addressed by the SLAM method [22]. Lidar-based SLAM suffers from cumulative errors in Lidar odometry [23]. To mitigate this issue, Lidar is commonly combined with Inertial Measurement Unit (IMU) for vehicle pose estimation. The integration of Lidar and IMU extends the method's applicability [24], offering reliable localization information even in indoor environments. The combination of Lidar and IMU can be categorized into loose coupling and tight coupling. Loose coupling involves separate Lidar-based and IMU-based estimations, fused using techniques like extended Kalman filters or factor graphs in pose estimation, ensuring high computational efficiency [25]. In contrast, tightly coupled Lidar and pre-integration IMU is integrated into a unified objective function during front-end measurement. This approach robustly estimates sensor trajectories in environments with fewer features, achieving high-precision localization and mapping [26], crucial for robust automatic parking space recognition. Leading tightly coupled Lidar-IMU odometry methods include LID-SAM [27], LIC-Fusion [28],In point cloud SLAM, the point cloud registration process is crucial. Point cloud registration encompasses both traditional ICP (Iterative Closest Point) and its improved variants and registration methods based on deep learning techniques. The ICP algorithm [33] is one of the most classic methods for point cloud registration. Its fundamental approach minimizes the distance error between the source and target point clouds through iterative optimization. Various improvements to the ICP algorithm have been developed to address specific application scenarios, including GICP (Generalized ICP) [34], NDT (Normal Distributions Transform) [35], and so on. With the advancement of deep learning technology, these methods leverage deep learning to handle noise, occlusion, and complex scenes by learning point cloud data feature representations. For instance, PointNetLK [36] utilizes PointNet for point cloud registration tasks and has shown promising results. DeepGMR (Deep Gaussian Mixture Model RANSAC) [37] combines deep learning feature extraction with the RANSAC algorithm to improve registration accuracy and robustness in noisy point cloud data. Despite these advances, deep learning-based registration methods typically require extensive data annotation and model training.