# An Intelligent Measurement Method and System for Vehicle Passing Angles
## 4 Experiment
As shown in Figure 12, we found that the point cloud built by the ICP registration tool in PCL [20; 31] (Point Cloud Library) exhibited an obvious alignment error in Figure 12a, and the method [22] failed to build a point cloud, as shown in Figure 12b, making it difficult to identify. The red square represents odometry data, which further confirms the failure as the wheeled robot is moving straight during the acquisition. Figure 12c,d show different views of the point cloud created by the method in [21], demonstrating distortion. On the other hand, Figure 12e,f show a better alignment result and an overall point cloud with improved geometric quality obtained using our method for parameter calculation. This is attributed to the close proximity of the wheeled robot to the ground during the acquisition of point cloud data from the underside of the vehicle, resulting in a small area of interest on the vehicle and a limited number of points. The introduction of a significant amount of error occurs during the ICP calculation process, and the circular shape of the ground points tends to lead to local optima during frame matching, particularly in the roll direction. Additionally, the scarcity of points on the vehicle, especially in terms of corner features, combined with the participation of numerous plane features from the ground points in the matching calculation, makes the matching process difficult to converge and even results in complete failure. The effectiveness of the odometry module in building point clouds in our method is evaluated and compared with the laser odometry. As shown in Figure 13a, the laser lines in the marked area exhibit a significant tilt when the point cloud is created using the laser odometry. Conversely, the point cloud using the odometry reduces the tilted laser lines in Figure 13b, indicating its advantage in enhancing the constraint in the pitch direction and reducing error. This improvement is attributed to the matching process in visual odometry, where the feature points are mainly concentrated in the region of the vehicle, providing stronger constraints in the pitch direction. Figure 14 shows the comparison of ground segmentation and the removal of outlier points in our method. In Figure 14a, the presence of floating shadows is caused by misalignment, while the jagged edges of the tires result from outliers and other interference points when directly aligning with laser feature points. Figure 14b,c show the point cloud results after solely removing ground points or outliers, respectively. It can be observed that only removing ground points or outliers leads to alignment errors in different directions and degrees. Figure 14d shows the overall point cloud in our method after reducing the proportion of ground points and removing outliers. This results in the effective reduction of floating shadows and jagged edges, thereby facilitating further processing of the point cloud. Figure 11: The results of static frame segmentation in data acquisition: (**a**) original laser point cloud frame, (**b**) vehicle part, (**c**) ground part. To verify the calculation method of the angles proposed in our study, we utilized a simulation vehicle model created in AutoCAD. The design of the module used for calculation is shown in Figure 15, and the results of the 10-time calculation for the simulation model are presented in Table 1.  
The results of the passing angles are shown in Table 1. We captured the point cloud of the vehicle's underside using a perspective view of the wheeled robot and calculated the angles using our proposed method. Since the truth-value of the vehicle is easily obtained in the simulation, we directly compared the calculated results with the simulated truth. The consistency of the calculated results was confirmed by performing 10 runs, with only negligible errors observed.