# Multi-Lidar System Localization and Mapping with Online Calibration
## 4 Real-World Experiment Validation and Result Analysis
### Experimental Results and Analysis of LiDAR SLAM on a Real Vehicle  
In this section, the proposed SLAM algorithm is compared with other LiDAR SLAM algorithms, and the absolute pose error (APE) is obtained by comparing the estimated trajectory of the SLAM algorithm with the GPS trajectory using the EVO tool, in order to verify the accuracy of the proposed SLAM algorithm. The trajectory comparison is shown in Figure 11. The experiment in this section involves a comparison between the proposed SLAM algorithm and prominent laser SLAM algorithms. The analysis is conducted using the EVO tool. The absolute pose error (APE) is obtained by comparing the GPS trajectory with the trajectory estimated by the SLAM algorithm to validate the accuracy of the SLAM algorithm proposed in this paper. This trajectory comparison is illustrated in Figure 11.  
Figure 11: Comparison of trajectories of various laser SLAM algorithms with GPS trajectories. (**a**) Comparison of trajectories on different axes for various laser SLAM algorithms; (**b**) trajectory in the xy plane; (**c**) trajectory in the xz plane; and (**d**) trajectory in the yz plane.  
Based on the preceding analysis, it becomes evident that, apart from the A-LOAM algorithm, the errors along the x and y axes of the other laser SLAM algorithms are relatively small. However, as historical feature points gradually vanish, the A-LOAM algorithm's absence of loop detection renders it unsuitable for large-scale scene mapping. In the \(z\)-axis direction, all algorithms exhibit notable errors; LeGO-LOAM and M-LOAM manifest the most substantial deviations. In contrast, the SLAM algorithm presented in this paper effectively curtails z-axis errors by incorporating ground constraints, leading to the smallest error among the surveyed laser SLAM algorithms.  
The APE and RPE outcomes output by the EVO tool underscore the APE values for each laser SLAM algorithm. Table 4 quantifies the conformity between the estimated and actual trajectories by evaluating absolute pose estimation.  
These APE statistical data provide a tangible insight into the positioning accuracy of each SLAM algorithm. Using the mean average APE output as a benchmark, the algorithm outlined in this paper emerges as the most adept in trajectory estimation, due to the integration of constraints that heighten the accuracy potential of the laser SLAM system.  
RPE pertains to the comparison of relative pose errors across different trajectories through attitude increments. It involves an ongoing juxtaposition between real and estimated trajectories, culminating in a localized trajectory accuracy assessment and a gauge of the SLAM system's drift. This paper considers both rotational and translational errors. Figure 12 and Table 5 present the RPE values of each laser SLAM algorithm.  
The analysis of RPE statistical data unveils that while the absolute pose accuracy of the proposed algorithm surpasses that of the other laser SLAM algorithms, its relative pose accuracy lags behind, exhibiting an approximate discrepancy of 0.3 m compared to other algorithms. This observation suggests that the proposed algorithm carries a certain level of local accuracy drift.