## 3 Experiment  
To evaluate the positioning accuracy and performance of the proposed integration model of multi-GNSS PPP-RTK/INS/vision with a cascading filter in urban areas, based on the FUSING (FUSing IN Gnss) software [7; 40; 41], this algorithm was further developed by us. At present, FUSING has been developed into an integrated software platform that can deal with real-time multi-GNSS precise orbit determination, atmospheric delay modeling and monitoring, satellite clock estimation, as well as multi-sensor fusion navigation. Two datasets were collected based on a vehicle platform as shown in Figure 2. One is in the suburban area of Wuhan City on 1 January 2020, and the other is on the Second Ring Road of Wuhan city on 2 January 2020. For simplicity, according to the DOY (day of the year), the two-vehicle tests are denoted as T01 and T02, respectively. As shown in Figure 2, the raw data was collected by the IMU of two different grades. A MEMS-grade IMU was used to integrate with PPP-RTK and vision to evaluate the performance of the proposed model. The navigation-grade IMU is of high accuracy and is integrated with RTK to calculate the reference solution, which was regarded as the true value. Both IMUs collected the data at a sampling rate of 200 Hz and their performance parameters are listed in Table 1. The grayscale Basler acA640-90 gm camera was equipped to collect the raw images at a sampling rate of 10 Hz with a resolution of 659 \(\times\) 494. The UBLOX-M8T was used for generating the pulses per second (PPS) to trigger the camera exposure and it also recorded the time of the pulse at the same time. GNSS data were collected by Trimble Net R9 at a sampling rate of 1 Hz. The camera-IMU extrinsic parameters were calibrated offline by utilizing the Kalibr tool ([[https://github.com/ethz-asl/kalibr/](https://github.com/ethz-asl/kalibr/)]([https://github.com/ethz-asl/kalibr/](https://github.com/ethz-asl/kalibr/)) (accessed on 3 January 2020)). The lever-arm correction vector was measured manually. Figure 1: The algorithm structure of PPP-RTK/INS/VISION with a cascading filter. Figure 2: GNSS/INS/vision data collection platform. The test trajectory and the true scenarios for these two tests are shown in the left and right panels of Figure 3, respectively. It can be seen that dataset T01 was collected in a relatively open sky and only a few obstacles were blocking the GNSS signals. T02 was collected in a GNSS-challenged environment and there were many tall buildings on both sides of the narrow road, including some viaducts and tunnels, which could have totally blocked the GNSS signals. The vehicle speeds of T01 and T02 were about 10 m/s and 15 m/s, respectively, which is shown in Figure 4. It can be seen that there were significant changes in velocity and direction. The number of visible satellites and the PDOP (precision dilution of positioning) with a cutoff angle of 10\({}^{\circ}\) are shown in Figure 5. Taking, for instance, the GPS, the average number of tracking satellites of T01 and T02 were 9 and 7, respectively, and the average PDOP values were 1.8 and 10.9, respectively, which demonstrates the difference in the observation environment between T01 and T02. Considering the high-precision ionospheric and tropospheric delay augmentation and ambiguity resolution to support PPP-RTK, the measurement data of seven reference stations as distributed in Figure 6 were also collected. They were processed to generate high-precision atmospheric delay corrections and UPD products. The average distance between the seven reference stations is 40 km and the green trajectories are the trajectories of the two tests as shown in Figure 3.  
The details of the GNSS data processing strategy are presented in Table 2. The positioning performance was evaluated by RMS (root mean square). The reference solution was calculated by a loosely coupled RTK/INS solution with a bi-directional smoothing algorithm, in which navigation-grade IMU and GNSS data collected by Trimble R9 were adopted.