# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 4 Experimental Verification
### Longchi Tunnel Scene Location Experiment Results  
The method proposed in this paper can still achieve good positioning results in tunnels without GNSS. A 1 km unmanned tunnel experiment was carried out in Longchi Tunnel, Dujiangyan City, Sichuan Province. The tunnel is well-lit and the lane lines are clear. In the tunnel scene, the unmanned vehicle has completed the navigation function, and its driving trajectory is as follows Figure 14:  
In the experiment, since there is no GNSS true value in the tunnel, the above error calculation cannot be performed. In this paper, the comparison between the driving trajectory and the trajectory of the tunnel itself was used for calculation. The specific method adopts the assumption of uniform speed to calculate the distance between the driving trajectory point and the road trajectory point. It can be seen from Figure 15 above that the positioning results are more accurate in the radian part of the tunnel. Due to the uniform speed assumption model, the results have a certain gap with the real results, but it can still clearly reflect the positioning of the method in this paper under certain changes in road observations. The effect is improved. The angle change in the second half of the tunnel is small, the observation and correction  
Figure 14: Tunnel without GNSS positioning trajectory. Figure 13: Experiments results in closed loop path. \begin{table}
\begin{tabular}{c c c c} \hline  & **Absolute Position** & **Relative Angular** & **Global Average** \\  & **Error Median/m** & **Error \({}^{\circ}\)/100 m** & **Positioning Error/m** \\ \hline Original SLAM & 3.2 & 0.34 & 3.45 \\ algorithm & 0.61 & 0.36 & 0.63 \\ \hline \end{tabular}
\end{table} 
Table 3: Error comparison. effect of the map is small, and the accumulated error is difficult to eliminate. After driving 600 m, the error reached m level, but the lateral local positioning results are still relatively stable. The distance between the driving trajectory and the road trajectory is calculated in the direction as the lateral positioning error. As shown in Figure 16, the lateral error positioning errors are all less than 0.35 m, and the initial positioning error is the error of the lane line positioning itself. Through the recalculation of the fusion positioning results, the positioning accuracy has been greatly improved and can be stabilized around 15 cm, which is sufficient. The unmanned vehicle completes the positioning and navigation in the fixed tunnel. It is worth noting that since there is no true value in the tunnel itself, the above two calculation methods are an approximate analysis, and the error between the true value and the positioning result is theoretically close to this method. Since the vehicle trajectory is smooth, the tunnel angle changes less, and the distance between the tangential directions and the actual point is less. Through the above analysis, there are no GNSS scenarios such as tunnels. After the fusion of map lane information and the SLAM positioning method, the positioning accuracy has been greatly improved, and it has a good effect in global and local positioning, as well as better stability. The algorithm proposed in this paper can effectively complete the navigation under local positioning. Compared with the SLAM algorithm itself, the global positioning accuracy is greatly improved, which has great advantages for actual navigation. This paper gives a summary of the following rules:  
1. When the method in this paper moves in a pure straight line, the effect of the fusion positioning is the worst, which is basically only the lag of the SLAM output result. Figure 16: Tunnel positioning transverse tangent error. Figure 15: Tunnel positioning trajectory error. 2. The fusion of the method in this paper can effectively suppress the divergence of positioning errors and ensure the positioning requirements of unmanned vehicles. The lateral positioning results are always stable within 20 cm, which is sufficient for unmanned navigation in the local coordinate system. 3.