## 5 Real Vehicle Experiments and Analysis of Results
### Experimental Results and Analysis  
Conduct real vehicle experiments using the collected dataset. Figure 7 shows the positioning and mapping results obtained by the experimental car driving around the teaching building. The entire trajectory did not experience any drift, and a dense 3D environmental point cloud was reconstructed with a clear outline. To preliminarily verify the accuracy of the trajectory obtained by this algorithm, the GPS trajectory during the driving process was obtained using combined inertial navigation as the true trajectory. As shown in Figure 8, the two trajectories have a high degree of overlap except for a slight deviation in position shown in Figure 8b. Figure 9 shows the localization and mapping result of the indoor long corridor. In the face of this scene with insufficient features and easy degradation, there was no positioning tilt or displacement occurred, and the constructed map has distinct edges that reproduce the long corridor environment very well.  
In order to evaluate the system's performance using a better dataset, the high-quality public dataset M2 DGR is adopted to compare this algorithm with several other mainstream LiDAR SLAM algorithms. The widely used M2 DGR dataset is provided by Shanghai Jiaotong University, which contains rich sensor suites and diverse scenarios. Moreover, the speed of the ground robot in each test sequence is close to the actual speed in real environments, which helps to evaluate the effects of various SLAM algorithms more fairly. The sequence 1 of the M2 DGR dataset is chosen for testing, with its scene being a relatively empty street and includes significant rotation during movement. Use the trajectory evaluation tool EVO to compare the proposed method with A-LOAM, Lego-LOAM, and  
Figure 6: Realistic environment of dataset collection. (**a**) The open space around the teaching building. (**b**) The satellite image of scene a, the green line indicates the complete trajectory of driving. (**c**) The environment map of long indoor corridor with lack of geometric features and high scene repetition.  
Figure 7: Positioning and mapping results around the teaching building.  
The positioning accuracy of each method is further evaluated through absolute trajectory error (ATE) and relative trajectory error (RPE). The ATE is the direct difference between the estimated pose and the ground truth pose, which can intuitively reflect the accuracy of positioning results and the global consistency of trajectories. The RPE is used to measure local accuracy and is used to measure the degree of drift of the positioning results. Tables 1 and 2 show the ATE and RPE values obtained by the four methods in sequence 1. There are seven indicators used to measure the accuracy of positioning results, including maximum error (max), mean error (mean), median error (median), minimum error (min), root mean square error (rmse), sum of squared errors (sse), and standard deviation (std). The values of each indicator are inversely proportional to the localization accuracy. Among them, the most commonly used and representative indicator are mean, median, and rmse. Therefore, we focus on these three indicators. As shown in Figure 12, for absolute trajectory error, the three error values obtained by proposed method are all the smallest. Since the backend of ALOAM is a grid-based map, the accumulated error after long-distance mapping is significant. However, Lego-LOAM incorporates a loop closure detection module, resulting in a significant reduction in the error. Compared with Lego-LOAM and LIO-SAM, the absolute positional error of proposed method is reduced by about 4.6% and 24%, respectively. For relative pose error, Lego LOAM has the largest error. The three error values of proposed method are very close to LIO-SAM, but there is still a decrease, and the rmse is also the smallest among the four methods. Compared to LOAM, Lego-LOAM, and LIO-SAM, the relative positional errors of proposed method are reduced by about 5.7%, 48%, and 0.3%, respectively.