# Multi-Lidar System Localization and Mapping with Online Calibration
## 4 Real-World Experiment Validation and Result Analysis
### Online Calibration Experiment Results and Analysis
#### 4.1.2 Comparison and Analysis of Online Calibration Algorithm Results for Multiple Lidars  
In the experiment, the proposed calibration algorithm was compared with multi_lidar_calibrator [35] and OpenCalib [36]. The data collected within the same scene were used for calibration using each algorithm. Calibration results were then analyzed to discern the strengths and weaknesses of the proposed algorithm within natural scenes. The results of the three calibration algorithms are shown in Figure 10.  
Visual assessment of the calibration outcomes unveils varying degrees of ineffectiveness in the other two calibration algorithms, and the proposed algorithm exhibits superior efficacy. By employing the improved RMSE method as described in this paper, along with the visualization results, calibration results from each algorithm are analyzed and compared to assess calibration quality. Statistical results are presented in Table 3.  
Through detailed analysis of experimental data, it can be concluded that the calibration algorithm proposed in this paper not only guarantees the point cloud registration accuracy, but also takes into account real-time performance, ensuring that when the lidar point cloud data is input at a frequency of 10 Hz, it can be processed in a timely manner, while the other two calibration algorithms have poor real-time performance and can only be used as offline calibration algorithms. At the same time, it can be clearly seen from the visualization calibration results that the point cloud calibrated by the multi_lidar_calibrator algorithm is not well registered, but its original RMSE indicator is the smallest, which means it has the highest accuracy. The improved RMSE indicator is consistent with the visualized results, verifying the feasibility of evaluating point cloud registration based on the improved  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm Name** & **Improved RMSE** & **Original RMSE** & **Visual Evaluation** & **Running Time** & **Corresponding Point Number** \\ \hline multi\_lidar\_calibrator & 2.194913 & 5.33469 & Poor & 14,121 ms & 1559 \\ \hline OpenCalib & 1.985486 & 8.4994 & Good & 5366.5 ms & 2069 \\ \hline my\_calibration & 0.914785 & 13.307 & Excellent & 98 ms & 3814 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed evaluation indicators for various multi-lidar calibration algorithms.  
Figure 10: Comparison of results from various multi-lidar calibration algorithms. (**a**) multi_lidar_calibrator; (**b**) OpenCalib; and (**c**) my_calibration.  
RMSE algorithm. A comprehensive analysis of experimental data underscores that the proposed calibration algorithm not only ensures accurate point cloud registration, but also balances real-time performance. It guarantees that when the lidar point cloud data is input at a frequency of 10 Hz, it can be processed promptly. Conversely, the real-time performance of the other two calibration algorithms is less satisfactory, relegating them to offline calibration use. Visual calibration results further highlight that point clouds calibrated by the multi_lidar_calibrator algorithm exhibit misalignment. However, the algorithm yields the smallest original RMSE index, indicating the highest accuracy. Notably, the improved RMSE index is consistent with the visual results, substantiating the feasibility of the point cloud registration based on the enhanced RMSE algorithm.