**6. Conclusions**

GNSS/INS integrated positioning is widely used in intelligent transportation systems (ITS). However, in urban canyons, due to the reception of NLOS and multipath, GNSS positioning performance is significantly affected, which in turn seriously affects the performance of GNSS/INS integrated positioning systems. First, a 3D LiDAR aided real-time fault detection algorithm was proposed. Then, a LiDAR aided real-time measurement noise estimation algorithm with an adaptive filter was proposed. Finally, the integrity of the proposed algorithms was assessed. To verify the performance of the proposed algorithm, experiments were carried out to compare it with the current method; the test scenario involved a vehicle going through a narrow viaduct and a wide floor hole in case one and case two, respectively. The experimental results were as follows.


the positioning errors, and the mean value of the error bounds was reduced by 53.03%. In case two, the error bound of the EKF could not overbound the positioning errors in 1490 epochs. However, the error bounds of the proposed algorithm could overbound the positioning errors in all epochs, and the mean value of the error bounds was reduced by 56.35%.

In general, the proposed algorithm can achieve significantly improved positioning performance in terms of accuracy and integrity. It is necessary to verify the performance of the proposed algorithms in different scenarios to satisfy industrial requirements, and this will be the focus of future work.

**Author Contributions:** Conceptualization, B.L. and K.F.; methodology, B.L. and Z.D.; validation, B.L.; formal analysis, B.L.; writing—original draft preparation, B.L.; writing—review and editing, H.W. and Z.W.; visualization, B.L.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was carried out with financial support from the National Key Research and Development Program of China (grant No. 2020YFB0505602), the National Natural Science Foundation of China (grant Nos. 61871012 and 62022012), the Civil Aviation Security Capacity Building Fund Project (grant Nos. CAAC Contract 2021(77) and CAAC Contract 2020(123)) and the Beijing Nova Program of Science and Technology (grant No. Z191100001119134).

**Data Availability Statement:** The raw/processed data required to reproduce these findings cannot be shared at this time, as the data also form part of an ongoing study.

**Acknowledgments:** The authors would like to thank the researchers at the National Key Laboratory of CNS/ATM for their advice and interests.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

