# An Intelligent Measurement Method and System for Vehicle Passing Angles
## 1 Introduction and Background
Additionally, the scale of the 3D models obtained using the Multi-View Stereo (MVS) approach needs to be adjusted to match real-world dimensions through camera-to-world scaling, introducing additional sources of error. Laser scanning is a convenient, non-contact detection method with advantages including the ability to scan millions of points rapidly and detect some areas that traditional tools cannot cover. Due to these advantages, laser scanning has become popular, and its application has extended into the field of monitoring, detection, and identification. [PERSON], [PERSON] et al. [12] proposed a method to compare the daily construction scaffold technique using LIDAR point clouds. Point clouds were also applied to detecting changes in confined building interiors [13] and for automated damage detection and evaluation in bridges [14]. [PERSON], [PERSON] et al. [15] also adopted this method to identify geometric design parameters of ISO 484-class propeller blades using scanned point-cloud data. Laser point clouds can directly represent the geometric information of objects and can be used for measurement and evaluation. An empirical study on LIDAR point density [16] analyzed the variation across different land covers, providing a reference for a convenient approach to land classi  
Figure 1: The schematic of the passing angle measurement. The approach angle is indicated by \(\alpha\), the ramp breakover angle is indicated by \(\beta\), the departure angle is indicated by \(\gamma\). fication. Researchers have also presented a method to evaluate the geometry of structures in underground mining based on LiDAR/Terrestrial Laser Scanner measurements [17]. The laser point cloud was also utilized to analyze the geometrical consistency of 3D-printed, cement-based materials with an as-designed modeled system [18]. Building point clouds is the foundation for measurements based on laser scanning. Scanning and registering point clouds using LIDAR is a universal point-cloud acquisition technique [19]. Many researchers have focused on how to build high-quality overall point clouds using different methods. An improved ICP algorithm was adopted by [PERSON], [PERSON] et al. [20] that used a corresponding accelerated method for point-cloud registration. [PERSON] [21] proposed a state-of-the-art open source method that builds an overall point cloud in real time by dividing feature points and registering them separately. [PERSON] and [PERSON] [22] presented a registration method with point segmentation to reduce grass interference. Principal component analysis was also used [23] to robustly extract features and build point clouds. Image information can complement some blind areas of laser scanning. The literature [24] presented better reconstructed 3D models by using laser scanning and photogrammetric data. Additionally, a more robust method [25] has been proposed using complement visual odometry to laser data. Manual measurement of passing angles requires several staff and tools to cooperate, resulting in high labor costs and low efficiency. The target points selected based on manual experience often deviate due to the various components on the vehicle chassis, and the selected area is sometimes not a regular plane. The deviations are magnified by the angle conversion, leading to poor data consistency in repeated testing by different staff, causing disputes between evaluated companies during competitive bidding. An automated approach to measuring passing angles based on laser scanning can improve the efficiency and robustness of the measurement and reduce potential disputes. However, based on long-term field measurements, we still found it difficult to conveniently obtain the relevant point cloud through existing technical means due to differences in the scanning scenarios. The relevant point cloud includes the point cloud of the vehicle chassis and the point cloud of the tires below the chassis. In this scenario, the height of the LIDAR that can pass underneath the vehicle is low, limiting the field of view (FOV) of the LIDAR.