# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 6 Conclusions and Future Research Direction  
This paper compares different GNSS/INS integration methods based on KF and FGO to process data from different levels of receivers (i.e., U-Blox, Xiaomi, and NovAtel). Despite being impacted by the quality of the trained deep learning model, the choice of uncertainty parameters (e.g., fixed, min, and max uncertainty), and limited duration (920s) with less evaluated epochs, the proposed MATLAB-implemented AFGO shows the promising capability to adaptively generate the most accurate (in terms of RMSE) and smooth (in terms of STD) solutions for all level of receivers among all discussed integration methods (i.e., KF, AKF, FGO, and AFGO).  
The current limitations partly come from a diversity of GNSS receivers because CNN models are trained separately across different receivers and are then employed to adaptively assist GNSS/INS integrations. Future work should include a generalized adaptation model in the AFGO architecture for all grades of receivers and should validate the feasibility and advantages of AFGO in a wider range of urbanization scenarios. Additionally, there can be large residuals between the truth and predicted solution uncertainty, indicating the necessity to propose better-functioning deep learning models. This could be potentially realized through various methods, including introducing more effective features, upgrading the network structure using methods such as optimizing the types and number of layers, and training through a larger amount of urban GNSS data. Moreover, the GNSS measurements like Doppler are related to the user dynamics. Thus, extending the proposed network for GNSS-based motion detection to compensate for INS error is worth investigating in the future. The implementation of the proposed algorithm in practical applications is also worth investigating in the future, where additional algorithms to reduce the computation load, such as the sliding window [11], may need to be developed.  
Conceptualization, Z.L., G.Z. and L.-T.H.; methodology, Z.L., P.-H.L. and G.Z.; software, Z.L., P.-H.L., G.Z. and L.-T.H.; validation, Z.L., P.-H.L. and G.Z.; formal analysis, Z.L., P.-H.L. and G.Z.; investigation, Z.L., P.-H.L. and T.H.M.H.; resources, Z.L. and P.-H.L.; data curation, Z.L.; writing--original draft preparation, Z.L., P.-H.L. and T.H.M.H.; writing--review and editing, Z.L., P.-H.L. and G.Z.; visualization, [PERSON] and P.-H.L.; supervision, G.Z. and L.-T.H.; project administration, Z.L., P.-H.L., T.H.M.H. and G.Z.; funding acquisition, G.Z. and L.-T.H. All authors have read and agreed to the published version of the manuscript.  
Figure 17: Zoom-in subfigures from Figure 16 during the period 162–167 (second) for the receiver of (a) U-Blox, (b) Xiaomi, and (c) NovAtel.  
**Funding:** This research is supported by the University Grants Committee of Hong Kong under the scheme Research Impact Fund on the project R5009-21, \"Reliable Multiagent Collaborative Global Navigation Satellite System Positioning for Intelligent Transportation Systems\".  
**Data Availability Statement:** The findings of this study were performed based on the open \"UrbanNav\" dataset at [[https://github.com/IPNL-POLYU/UrbanNavDataset](https://github.com/IPNL-POLYU/UrbanNavDataset)]([https://github.com/IPNL-POLYU/UrbanNavDataset](https://github.com/IPNL-POLYU/UrbanNavDataset)) (accessed on 1 December 2023). The open-source codes in this research with examples can be found at [[https://github.com/ZhengdaoI_I0602/GNSS_INS_Integrations_Comparisons](https://github.com/ZhengdaoI_I0602/GNSS_INS_Integrations_Comparisons)]([https://github.com/ZhengdaoI_I0602/GNSS_INS_Integrations_Comparisons](https://github.com/ZhengdaoI_I0602/GNSS_INS_Integrations_Comparisons)) (accessed on 1 December 2023).  
**Conflicts of Interest:** The authors declare no conflicts of interest.