## 2 Related Works  
Current parking space recognition and detection methods include pure vision approaches, pure Lidar approaches, vision-Lidar fusion techniques, and IMU-coupling methods. Image-based parking space detection methods face challenges such as insufficient lighting or unclear textures, leading to detection errors. As shown in Figure 2a, the texture boundary is relatively blurred at night. To address these limitations, [PERSON] et al. [13] introduced parking space semantic features and the concept of inverse exponential functions to improve closed-loop detection, thereby enhancing parking localization accuracy. However, localization accuracy in parking scenarios is not always closely correlated with the accuracy of parking space recognition. Deep learning-based methods can improve the accuracy of nighttime parking recognition; for example, [PERSON] et al. [4] developed a parking space detection method by training a deep learning network model with data from various lighting conditions. Nonetheless, they require extensive pre-training datasets, substantial training time, and significant computing power, and they incur high costs. Another approach involves image enhancement [14] to compensate for poor lighting. However, image preprocessing can alter pixel values, which may adversely affect the extraction of image features and result in only marginal improvements in parking space recognition. To address the parking challenges in scenes lacking clear or blurred lane lines under complex weather conditions, parking space detection methods based on 3D distance detection have been proposed. The Lidar-based 3D point cloud method is highly resistant to interference from light sources and shadows, providing very accurate 3D poses and  
Figure 2: Comparison between image vision and Lidar vision, data from nuScenes [15]. **(a)** Image vision in a night scene; **(b)** Lidar vision at the same time and place; **(c)** Superposition of image vision and laser vision. rich geometric information for environmental perception, as shown in Figure 2. Figure 2b shows the bird's-eye view (BEV) perspective of the Lidar 3D point cloud, corresponding to Figure 2a at the same time and place. Lidar detects the distance of objects using the time of flight (ToF) principle, recording their three-dimensional spatial information. The geometric structure information of the object can be derived from the 3D point cloud, which is then projected onto a two-dimensional (2D) image, as depicted in Figure 2c. In existing research, [PERSON] et al. [16] used Lidar to measure the reflection intensity of the road surface to identify white-painted parking spaces. This approach, however, is limited to scenarios with white-painted parking lines and lacks generalization across different environments. [PERSON] et al. [17] proposed a Lidar-based parking lot localization and mapping method incorporating obstacle semantic information. While this method reduces the workload for downstream parking space annotation, it involves deep learning, making the mapping tasks labor-intensive. [PERSON] et al. [18] performed parallelepiped detection to determine the occupancy status of parking spaces by analyzing the space above them. However, this method relies on prior information about the parking space location, making it unsuitable for unknown environments. [PERSON] et al. [19] used detected 3D bounding boxes to categorize parking spaces formed by adjacent bounding boxes, calculating the minimum free space distance to determine if the space is usable for parking. Despite its effectiveness, the 3D-based method introduces an additional dimension compared to 2D methods (such as images or 2D point clouds [20]), significantly increasing computational complexity and reducing efficiency in detecting free parking spaces. The method based on images with Lidar is another method to label parking spaces. In current image-Lidar fusion research progress, [PERSON] et al. [3] proposed a parking SLAM method utilizing vision-Lidar fusion.