# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 4 Experiments
### Outdoor Accuracy Evaluation  
To evaluate the localization accuracy of our proposed system, we conducted experiments on the NCLT dataset and compared the results with two representative SLAM algorithms: R\({}^{3}\)LIVE++ [4] and FAST-LIO2 [34]. None of the compared methods employed loop closure. Our method utilizes FAST-LIO2 as the odometry backbone.  
The experimental results are shown in Table 1, demonstrating that the proposed method significantly improves localization accuracy compared to R\({}^{3}\)LIVE++ and FAST-LIO2. This improvement is primarily attributed to the use of global localization based on image matching, which effectively eliminates error accumulation over time.  
It is worth noting that the original R\({}^{3}\)LIVE++ paper does not report experimental results on the 2012-03-25 sequence. Our experiments further indicate that Fast-LIO2 exhibits significant drift on this sequence, with an RMSE of 19.30 m. However, our proposed method effectively corrects the drift by integrating matching-based observations.  
Figure 6 presents a comparison between the proposed method and FAST-LIO2 on a representative segment from the NCLT dataset. As shown in Figure 6, the green, red, and blue lines represent the trajectories estimated by LIV-SLAM (FAST-LIO2), the proposed method, and the ground truth, respectively, while the yellow points indicate the localization results obtained from the matching algorithm. This color convention is consistently used throughout the paper. In Figure 6, the initial segment of a sequence from the NCLT dataset shows that FAST-LIO2 maintains low drift and achieves localization accuracy comparable to our method. However, in Figure 6, after approximately 1800 s, FAST-LIO2 begins to exhibit significant position drift due to accumulated odometry error. In contrast, our method continuously corrects this drift through matching-based global localization, thereby achieving improved overall localization accuracy.  
We also conducted comparative experiments on two sequences from the R\({}^{3}\)LIVE dataset, evaluating the proposed method against R\({}^{3}\)LIVE and Fast-LIVO2. Since the original R\({}^{3}\)LIVE, under default parameters, has been reported to achieve a localization  
Figure 6: Localization drift correction. (**a**) The initial segment of a sequence from the NCLT dataset where LIV-SLAM closely following the ground truth; (**b**) Significant drift occurs in LIV-SLAM as the sequence continues.  
accuracy of approximately 0.1 m [3], we use its trajectory as the ground truth reference for evaluation.  
Table 2 shows the localization accuracy of the three methods on the R\({}^{3}\)LIVE dataset, also demonstrating that the proposed approach achieves the minimum RMSE among all tested methods. In Table 2, R\({}^{3}\)LIVE- is a degraded version of R\({}^{3}\)LIVE, which operates with reduced point cloud resolution and a smaller number of tracked visual features, resulting in degraded odometry performance. When comparing with the outputs of R\({}^{3}\)LIVE-, the LIV-SLAM module implemented in our system is also R\({}^{3}\)LIVE-. Therefore, the observed improvements in localization accuracy are solely attributed to the proposed method.