## 4 Results
The positioning errors diverged to 21.74 m, 15.82 m, and 3.82 m in the north, east, and down directions, respectively. The three-dimensional positioning error at 362,965 s was 27.16 m, which is not suitable for vehicle navigation. Furthermore, the positioning error was obviously reduced when vision was included, especially around the time of the week at 362,900 s, 363,600 s, and 364,000 s. The cumulative  
Figure 8: The effects of dynamic feature points removal on positioning accuracy. errors of the INS were effectively constrained by vision. Thus, in the period from 362,906 s to 362,965 s, the maximum errors of the INS/vision were \(-\)1.43 m, \(-\)3.94 m, and 2.50 m in the north, east, and down directions, respectively. The corresponding three-dimensional error was 4.88 m, which was 0.49% of the traveled distance. It can be concluded that the integration of multi-GNSS PPP-RTK/INS/vision with a cascading filter performed best in comparison with the other four solutions. Figure 10: Position difference series of T02 in north, east, and down directions, respectively. Figure 9: Position difference series of T01 in north, east, and down directions, respectively. The statistics of the position difference between T01 and T02 are presented in Tables 3 and 4 to further verify the conclusion. Additionally, the number of epochs at which the position information can be obtained is denoted as A and the number of total epochs is denoted as B. Then the positioning availability can be defined as A/B, which is also included in the tables. The improvement statistics are derived in comparison with the G-PPP solution. The statistics show that the PPP-RTK performed better than PPP in both tests. The ambiguity could not be fixed in many epochs for the frequent GPS signal interruptions and disturbances in T02. Thus, the improvement brought by PPP-RTK for T02 was not as obvious as for T01. The horizontal and vertical positioning RMS of the GC-PPP-RTK/INS/vision solution for the test T01 were 0.08 m and 0.09 m, respectively. As for T02, the horizontal and vertical RMS of the GC-PPP-RTK/INS/vision solution were 0.83 m and 0.91 m, respectively. It can be seen that GC-PPP-RTK/INS/vision made significant improvements compared with the other four solutions. The improvements should have been more obvious because of the interruption in G-PPP and G-PPP-RTK. The positioning availabilities of G-PPP and G-PPP-RTK were both 90.5%. The availability increased to 95.1% with the inclusion of BDS, which means that more epochs of worse positioning were taken into consideration. Because of the accumulation errors of the INS, the statistics of GC-PPP-RTK/INS were worse than all the other solutions in the horizontal direction. The statistics of GC-PPP-RTK, GC-PPP-RTK/INS, and GC-PPP-RTK/INS/vision are shown in Table 5, in which the positioning results derived by the INS and vision are excluded, in order to better show the improvements brought by the INS and vision to the positioning performance of GC-PPP-RTK. The improvement statistics were derived in comparison with the GC-PPP-RTK solution. It can be seen that the INS improved the performance of GC-PPP-RTK by 31.4% and 37.1% in the horizontal and vertical directions, respectively. Eventually, the inclusion of vision increased the improvements to 37.1% and 42.2% in the horizontal and vertical directions, respectively. Figure 11: Convergence (left panel) and reconvergence (middle panel and right panel) of T02 in north, east, and down directions, respectively. Position, velocity, and attitude are of great importance for vehicle navigation in urban areas. The velocity error series of GC-PPP-RTK/INS and GC-PPP-RTK/INS/vision for T01 and T02 are shown in Figures 12 and 13, respectively. As dataset T01 was collected in a relatively open-sky environment, the error series was very stable. There was no obvious difference between GC-PPP-RTK/INS and GC-PPP-RTK/INS/vision.