## 4 Results  
All results were output at a frequency of 1 Hz. In the following analysis, PPP-RTK/INS means the tightly coupled integration of PPP-RTK and INS. PPP-RTK/INS/vision means the integration of PPP-RTK, INS, and vision with a cascading filter. Before analyzing the performance of our proposed integration system in urban areas, the effects of the dynamic feature point removal algorithm aided by position are presented in Figure 7. It can be seen that the feature points on the car (in the red box) were removed. Some points that were extracted from unobvious places were also removed and the most static obvious feature points were saved, which can be used for visual localization. Figure 8 shows the positioning results of PPP-RTK/INS/vision for T02 before and after dynamic feature point removal. The positioning accuracy was improved by dynamic feature point removal, which is demonstrated in the green box in Figure 8. When PPP-RTK/INS provided stable, high-accuracy position information, the error caused by the dynamic feature points was constrained. However, the positioning performance was obviously influenced by the dynamic feature points when the GNSS signals were interfered with. Combining Figures 5 and 8, the GNSS observation conditions were poor and PPP-RTK/INS performed poorly in positioning around time 362,900 s, 364,000 s, and 364,600 s, thus PPP-RTK/INS was not able to restrain the interference of the dynamic feature points. Therefore, the dynamic feature points removal algorithm based on position improved the positioning accuracy when the GNSS signals were severely disturbed. The statistics of the positioning results show that the positioning accuracy was improved by 3 cm and 1 cm in the horizontal and vertical directions, respectively. The comparison of the performance of different positioning solutions is presented in Figures 9 and 10 for T01 and T02, respectively. It can be seen in the left section in Figure 9 that PPP-RTK converged much faster than PPP, though the ambiguity sometimes failed to fix. It took about 30 s to converge for PPP-RTK and more than 10 min for PPP. Moreover, PPP-RTK had higher accuracy than PPP after convergence. The inclusion of BDS made the series more stable, especially for the vertical direction. Although the contribution of the INS was rather limited in the horizontal direction as shown in the GC-PPP-RTK/INS solution, the outliers may have been inhibited, e.g., around the time 28,400 s. As for the vertical direction, the INS significantly contributed to the improvement of the positioning accuracy. The INS helped the GNSS to converge to and maintain a higher level of positioning accuracy. Additionally, the introduction of vision reduced the fluctuation, although overall, there was no big difference. As for T02, there was no obvious convergence in Figure 10 as the observation environment was complicated and the positioning accuracy was relatively low. However, it still can be seen in the left section in Figure 10 that PPP-RTK performed better than PPP. In order to further demonstrate the convergence and reconvergence effects of PPP and PPP-RTK, enlarged images of parts of Figure 10 are shown in Figure 11 and the correspondence can be seen from the time of the week. PPP-RTK converged and reconverged much faster than PPP and achieved higher positioning accuracy, though the observational environment was challenging. Figure 10 shows that the series of G-PPP and G-PPP-RTK were interrupted many times because the GNSS signals were blocked out, which is embodied in the tracking number of the visible satellites in Figure 5. Both the continuity and accuracy of the positioning were improved with the BDS included. However, the GNSS was still unable to provide positioning results from 362,906 s to 362,965 s because the vehicle was in the tunnel at that time and there was no GNSS signal at all. GC-PPP-RTK/INS provided continuous and more stable positioning information with the INS, but there was also obvious fluctuation and the existence of epochs with large positioning errors.