# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 5 Testing and Result Analysis
In addition, we compare the low-cost lightweight crowdsourcing mapping method proposed in this paper with the two representative crowdsourcing cloud mapping methods in previous studies, such as the methods proposed by [PERSON] et al. [14] and [PERSON] et al. [15], respectively. The comparison of the accuracy, completeness, and overall map construction efficiency of the three fusion mapping algorithms for different semantic elements is shown in Table 3. It is shown that our method has the lowest accuracy error, higher factor completion rate, and relatively high efficiency. We analyzed the completeness rate and redundancy rate of different semantic elements and found that the ground elements, such as lane line, ground arrow LaneMarking, and StopLine, were slightly better than air elements, such as TrafficSign and Pole, as shown in Figure 8. This is because the quality of ground elements in the LSM generated on-vehicle is better than that of air elements. The comparative analysis of the influence of different observation times on fusion mapping on-cloud is shown in Figure 9. The results indicate that the accuracy of map elements improves as the observation time increases. For instance, the relative error of 50 observation fusions is over three times lower than that of 10 fusion mapping, and the element completeness rate is over double. When 100 fusions are reached, the relative accuracy of the lane line is close to 10 cm, and that of other elements is within 50 cm. Additionally, the completeness rate of elements is 95% on average, which has reached the mapping level of traditional high-precision sensors and can meet the needs of high-level \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **LaneLine** & **LaneMarking** & **TrafficSign** & **Pole** \\ \hline ICP & 0.45 & 2.25 & 2.51 & 2.18 \\ \hline SICP & 0.23 & 1.24 & 1.76 & 1.35 \\ \hline SGICP & 0.19 & 0.72 & 0.83 & 0.66 \\ \hline GBA_SGICP & 0.17 & 0.49 & 0.58 & 0.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Impact of cloud alignment algorithm on mapping accuracy (3-sigma avg.m). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **ICP** & **SICP** & **SGICP** & **GBA_SGICP** \\ \hline Efficiency & 6.4 & 10.2 & 13.5 & 17.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Impact of cloud alignment algorithm on mapping efficiency (unit: km/h). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Object** & \multicolumn{2}{c}{**LaneLine**} & \multicolumn{2}{c}{**LaneMarking**} & \multicolumn{2}{c}{**TrafficSign**} & \multicolumn{2}{c}{**Pole**} & \multirow{2}{*}{**Efficiency**} \\ \hline
**Algorithm** & **Acc** & **Compl** & **Acc** & **Compl** & **Acc** & **Compl** & **Acc** & **Compl** \\ \hline [PERSON]’s method & 0.48 & 0.88 & 2.34 & 0.94 & 2.22 & - & - & - & 19.5 \\ \hline [PERSON]’s method & 0.20 & 0.93 & 1.21 & 0.85 & 1.58 & - & 1.28 & - & 3.8 \\ \hline Our method & 0.17 & 0.96 & 0.49 & 0.95 & 0.58 & 0.89 & 0.51 & 0.91 & 17.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of three crowdsourced mapping methods. autonomous driving. Therefore, it can be concluded that increasing the observation time can significantly improve the accuracy and completeness of fusion mapping. We conducted an analysis of crowdsourced fusion mapping on a section of an urban road in an actual scene, and the results are presented in Figure 10. It is observed that due to the accuracy limitations of low-cost sensors, even though LMS data has been transferred to the global coordinate system through pose graph optimization, large errors still exist in multiple observation results of the same road section as shown in Figure 10a. However, after the optimization of semantic alignment, the location consistency of semantic elements observed many times has been greatly improved as shown in Figure 10b.