# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 4 Experimental Verification
### Campus Scene Localization Experiment Results  
This experiment was carried out in the campus area. The real map lane value was obtained by the data acquisition car driving along the lane, and it can be considered that there is no map lane information error. GNSS was used as the true value of the data to calculate the original SLAM method, the SLAM algorithm with relocation information, and the positioning results of lane line positioning at this time, and to compare them with the true value of GNSS. The positioning results are as follows.  
As shown in Figure 9, the two lines represent the true value of GNSS and the result obtained by the original SLAM algorithm. Here, the algorithm adopts the VINS-Mono algorithm, and the positioning result is displayed on the map after the coordinate system transformation. The true value of the map is displayed. It can clearly be seen from Figure 9  
Figure 8: Distance \(D\) positioning error. (**left**): the change of error with the number of frames; (**right**): the distribution of distance error.  
that the effect of the algorithm in this paper is better than the original SLAM method in practical application, and its positioning results basically coincide with the true value. It has a great influence, so the experiment is divided into two motion states of straight line and non-pure straight line.  
When the lane is not a pure straight road, that is, the road has a radian, the \(Q\) matrix in the equation can be updated to obtain an accurate estimate of the position, and the vehicle trajectory and the road trajectory can be fused by adjusting the coefficient. The results of the positioning experiment and the schematic diagram are shown in Figure 10.  
In detours, the divergence of SLAM positioning results is better limited, the cumulative error is eliminated, and the positioning results of \(<\)1 m are obtained in gentle detours, and the effect is good.  
As shown in Figure 11, In the straight-line experiment, since the lane line positioning information does not change greatly, the state quantity of the positioning cannot be corrected by observation, and the fusion positioning result at this time is expressed as the output lag of SLAM. The method proposed in this paper relies on the effective map information. When the pure linear motion has a lack of effective observation, the error cannot be corrected.  
Figure 11: Absolute positioning error of straight road test.  
Figure 9: On-campus location results.  
However, in pure linear motion, although the error cannot be corrected by map information, the lateral positioning accuracy can still meet the needs of vehicle navigation. The results of lateral local positioning information are shown in Figure 12.  
As shown in Figure 12, the local positioning error does not exceed 0.3 m in most cases. When moving in a straight line, the lane can be cruised through local positioning. When encountering a detour, due to the fast-converging observation method set in the previous article, it can quickly converge to the location where it is located.  
To summarize, the algorithm proposed in this paper has a good positioning effect on non-pure straight roads, and the positioning accuracy is greatly improved, while in pure straight line motion, the algorithm accuracy is less improved, but the results of lateral local positioning meet the requirements of unmanned driving. As shown in Table 2, the numerical analysis is as follows:  
As shown in the table above, in the data experiment of detours, the localization algorithm after integrating map information achieved good results, the global positioning accuracy improved by more than 70%, and the local positioning was also significantly improved, which is more suitable for behavior scenarios involving unmanned positioning use.