## 6 Conclusions  
To improve the position, velocity, and attitude estimation performance in urban areas for vehicle navigation, a multi-GNSS PPP-RTK/INS/vision integration model with a cascading filter was developed and validated using two vehicular tests, T01 and T02, in urban areas. T01 was conducted in the suburban area of Wuhan City and T02 on the Second Ring Road of Wuhan city. The T02 test can be regarded as a typical GNSS-challenged environment. To obtain the atmospheric corrections and UPD products for PPP-RTK, observations from seven reference stations were also collected for generating those products. A dynamic object removal model was also proposed and validated with T02. A dynamic object removal model based on position can work well in a GNSS-challenged environment and improve the positioning performance of multi-GNSS PPP-RTK/INS/vision. PPP-RTK achieved centimeter-level positioning in the horizontal direction and decimeter-level positioning in the vertical direction under a relatively open sky environment such as T01. The performance in the vertical direction was obviously improved when BDS was included with respect to G-PPP-RTK. Moreover, it took only about 30 s for PPP-RTK convergence due to the atmospheric augmentation and ambiguity resolution. However, incorrect ambiguity resolution remained and the position performance became significantly worse in this case. The introduction of the INS weakened the influence of the incorrect \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Solution** & **Roll [\({}^{\circ}\)]** & **Pitch [\({}^{\circ}\)]** & **Yaw [\({}^{\circ}\)]** & **Improvement** \\ \hline GC-PPP-RTK/INS & 0.05 & 0.04 & 0.24 & & \\ GC-PPP-RTK/INS/Vision & 0.05 & 0.04 & 0.11 & 0\% & 0\% & 54.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: RMS of the attitude error of T01 for GC-PPP-RTK/INS and GC-PPP-RTK/INS/vision. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Solution** & **Roll [\({}^{\circ}\)]** & **Pitch [\({}^{\circ}\)]** & **Yaw [\({}^{\circ}\)]** & **Improvement** \\ \hline GC-PPP-RTK/INS & 0.07 & 0.06 & 0.39 & & \\ GC-PPP-RTK/INS/Vision & 0.05 & 0.06 & 0.26 & 28.6\% & 0\% & 33.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: RMS of the attitude error of T02 for GC-PPP-RTK/INS and GC-PPP-RTK/INS/vision. ambiguity resolution and improved the positioning accuracy. The positioning error of GC-PPP-RTK/INS was 0.08 m and 0.09 m, with an improvement of 11.1% and 59.1% in the horizontal and vertical directions, respectively, in comparison with GC-PPP-RTK. The performance of PPP-RTK degraded fast when the GNSS observation environments became complicated and challenging such as T02. G-PPP-RTK could only achieve meter-level positioning in a GNSS-challenged environment. Compared with G-PPP-RTK, the positioning availability was improved from 90.5% to 95.1% for GC PPP-RTK. The GC-PPP-RTK/INS solution, in comparison with the GC-PPP-RTK solution, contributed significantly to the improvement of the positioning accuracy in the horizontal and vertical directions and the positioning availability. The positioning availability of GC-PPP-RTK/INS increased to 100%. The positioning error of GC-PPP-RTK/INS was 0.48 m and 0.73 m in the horizontal and vertical directions, respectively, after excluding the positioning results derived by the INS. The improvements were 31.4% and 37.1% in the horizontal and vertical directions, respectively, in comparison with GC-PPP-RTK. The two-vehicle tests showed that GC-PPP-RTK/INS could realize high-precision continuous positioning in a relatively open-sky environment but the positioning errors diverged to more than 20 m in a GNSS-challenged environment. Thus, it needed other sensors such as vision to help restrict the error divergence. Vision did not improve the positioning accuracy statistically but further reduced the fluctuation slightly in the vertical direction for T01.