# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 5 Conclusions  
This paper presents a global localization algorithm based on real-time map projection and cross-modal image matching, which can effectively correct the drift in existing LIV-SLAM systems such as Fast-LIO, R\({}^{3}\)LIVE++, and Fast-LIVO2. In addition, we propose an AKF incorporating MC and OAGS mechanisms, which fuses the outputs of the LIV-SLAM module with global localization results to achieve a novel and practical long-distance autonomous navigation approach for land vehicles. Compared to existing cross view-based autonomous navigation approaches [32], which achieves an average localization accuracy of about 20 m and operates at a relatively low frequency of 0.5-1 Hz, the proposed method achieves pixel-level alignment, resulting in significantly improved localization accuracy (RMSE of about 2 m) and real-time performance (approximately 14 Hz with OAGS enabled and around 30 Hz without it). However, the proposed method has several limitations. First, it requires accumulating a sufficient amount of point cloud data before generating a projection image, thus it cannot support one-shot localization. Second, the system relies on the output of a LIV-SLAM module; if the SLAM fails completely (e.g., due to extreme motion or sensor degradation), the localization may fail. Additionally, in environments lacking structural or texture features, such as open grasslands or deserts, the image matching process may be unreliable. Future work will focus on extending the proposed framework to a purely vision-based solution that eliminates the dependency on LiDAR sensors. This adaptation will improve the system's applicability to a broader range of platforms. The Supplementary Materials which provide trajectory plots of the proposed method on all test sequences and a demo video will be released through GitHub ([[https://github.com/yao-yating/RGL-LIV-SLAM](https://github.com/yao-yating/RGL-LIV-SLAM)]([https://github.com/yao-yating/RGL-LIV-SLAM](https://github.com/yao-yating/RGL-LIV-SLAM)), accessed on 7 June 2025) and BaiduDisk ([[https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) qqi3u7 oM1 qBKZJlzg42Q?pwd=sa6q]([https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) qqi3u7 oM1 qBKZJlzg42Q?pwd=sa6q), accessed on 7 June 2025). The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15158257/s1](https://www.mdpi.com/article/10.3390/app15158257/s1)]([https://www.mdpi.com/article/10.3390/app15158257/s1](https://www.mdpi.com/article/10.3390/app15158257/s1)), Figure S1: The similarity metric functions; Figure S2: Result on 2012-02-02; Figure S3: Result on 2012-02-04; Figure S4: Result on 2012-02-05; Figure S5: Result on 2012-02-19; Figure S6: Result on 2012-03-17; Figure S7: Result on 2012-03-25; Figure S8: Result on 2012-04-29; Figure S9: Result on 2012-05-11; Figure S10: Result on 2012-06-15; Figure S11: Result on 2012-08-04; Figure S12: Result on 2012-10-28; Figure S13: Result on 2012-11-17; Figure S14: Result on hkust_seq00; Figure S15: Result on hkust_seq01; Video S1: Result on Sequence NCLT-2012-02-04. Conceptualization, J.D. ; methodology, Y.Y. and J.D. ; software, Y.Y. and J.D. ; validation, Y.Y., Q.H. and Z.C. ; formal analysis, Y.Y. and J.D. ; investigation, Y.Y. and J.D. ; resources, J.D. ; data curation, Y.Y., Q.H. and Z.C. ; writing--original draft preparation, Y.Y. and J.D. ; writing--review and editing, J.D., S.H. and H.L. ; visualization, Y.Y., J.D. and Q.H. ; supervision, J.D. and S.H. ; project administration, J.D. and S.H. ; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript. This work is in part supported by the National Natural Science Foundation of China under Grant 62203163, in part supported by the National Natural Science Foundation of Hunan Province under Grant 2025 JJ60407 and in part supported by the National Key Research and Development Program of China under Grant No. 2023 YFC2907000. **Institutional Review Board Statement:** Not applicable.