# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 5 Testing and Result Analysis  
We conducted experiments, using crowdsourced data collected in real-world scenarios, to evaluate the effectiveness of our proposed GBA_SGICP algorithm and the capacity of fusion mapping on a cloud server. For this experiment, we used three vehicles equipped with the same sensors, including GPS, IMU, wheel speed encoder, and monocular camera, to collect data. The test scenario was a typical complex overpass scenario, as shown in Figure 7.  
To evaluate the quality of our automated crowdsourced mapping approach, we used the map produced by high-precision equipment acquisition vehicles and manually verified it as the ground truth. We used the following evaluation metrics: relative precision, semantic completeness, and semantic redundancy. Relative precision measures the accuracy of the relative position relationship between map elements, such as the lane width, the distance between two adjacent poles, etc. It is an important indicator to measure whether the map accuracy meets the requirements of autonomous driving. In this paper, we define relative precision as a comprehensive index of horizontal and vertical precision. The formula for calculating the relative precision of the same type of semantic elements is as follows:  
\[\text{relative precision}=\frac{\sum_{i=1}^{N}\left|\mathbf{rd}_{i}^{obs}-\mathbf{rd}_{i }^{truth}\right|}{N}\]  
where \(\mathbf{N}\) is the total number of semantic elements of the same type in the crowdsourced map, \(\mathbf{rd}_{i}^{obs}\) is the relative distance of \(\mathbf{i}\)-th elements of the same type in the crowdsourced map, and \(\mathbf{rd}_{i}^{truth}\) is the true value of the corresponding point. Semantic element completeness rate: It measures the proportion of map elements (lane line, traffic sign, traffic light, etc.) in the real world. It is also an important indicator of the automatic driving positioning system. The formula for calculating the completeness rate of the same type of semantic elements is  
\[\text{semantic element completeness rate}=\frac{\sum_{i=1}^{N}\mathbf{Num}_{i}^{ obs}(\sum_{i=1}^{N}\mathbf{Len}_{i}^{obs})}{N(\sum_{i=1}^{N}\mathbf{Len}_{i}^{truth})}\]  
Semantic element redundancy rate: It refers to the proportion of elements constructed from maps that do not exist in the real world. It measures the degree of incorrect construction of map elements. The formula for calculating the redundancy rate of the same type of semantic elements can be expressed as:  
\[\text{semantic element redundancy rate}=\frac{\mathbf{Sum\_Num}^{ obs}(\mathbf{Sum\_Len}^{obs})}{N(\sum_{i=1}^{N}\mathbf{Len}_{i}^{truth})}\]  
For discrete-type elements (such as signboards), the calculation formula is \(\mathbf{Sum\_Num}^{obs}\mathbf{N}\); for continuous type elements (such as lane lines), the calculation formula is \(\mathbf{Sum\_Len}^{obs}\)\(\mathbf{/}\sum_{i=1}^{N}\mathbf{Len}_{i}^{truth}\), where \(\mathbf{N}\) is the number of instances of semantic elements of the same type that actually exist in the real world, and \(\mathbf{Sum\_Num}^{obs}\mathbf{/Sum\_Len}^{obs}\) represents the number of elements that do not exist in the real world but exist in the map construction. We conducted a comparison between our proposed GBA_SGICP algorithm and different alignment algorithms using the same dataset, and with a data concentration of 50 times  
Figure 7: Test scenario. for each road. We evaluated the mapping accuracy of four alignment algorithms on four semantic elements, including two ground and two air elements, as shown in Table 1. The results indicate that our proposed GBA_SGICP algorithm achieves the highest relative accuracy compared to the other alignment algorithms. Table 2 presents the impact of the four alignment algorithms on the mapping efficiency using the average values of the efficiency of various scenes, including high-speed and urban scenes. The results show that the GBA_SGICP algorithm can achieve a mapping efficiency of nearly 18 km/h, which is about three times higher than the ICP alignment algorithm.