# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 6 Experiment and Results
### Results and Interpretations
During this portion of the trajectory (mostly with one side of the road non-obstructed), after excluding NLOS satellites, four satellites remain (see Figure 17), and the TC GNSS/vision integrated solution is relatively accurate. However, the second portion of the trajectory that contains a mixture of short and tall (\(\geq\)100 m tall; see Figure 14) buildings (Paths 2, 3, 4 and 5 in Figure 15) is a bit more challenging for two reasons: (i) the first reason is the height of the buildings. Downtown Calgary buildings in general range from 20-50 stories with roads about 15 m wide [64], and even if measurements pass the NLOS selection process, the multipath effect is expected to be large. (ii) The second reason is that the vehicle \"stops\". There was severe traffic during this portion of the test. The vehicle was frequently stopping and starting. Since feature points were detected on the sky (and \"non-static\" clouds), this influenced the accuracy of the visual odometry outputs. When these two phenomena were occurring at the same location, the integrated solution was degraded as shown in Figures 16 and 18. From around Zone A, to the end of the travelled path, there are less than four satellites (after NLOS exclusion) 75.8% of the time. For this reason, the LC GNSS/vision integration, which in this situation relies on the vision system only, performs poorly, while the proposed TC GNSS/vision closely follows the reference solution. Overall, the proposed method provides consistent accuracy over the travelled path with a few exceptions at the turns. At Zones A and B, we can observe that the proposed method does not drift too much while the GNSS-KF provides poor solutions. From Figure 18, it is seen that the solution obtained from the LC GNSS/vision outputs larger error compared to the two other approaches. In Zone A (Figure 16), after NLOS satellites exclusion, there are constantly less than four satellites (Figure 16). As a result, GNSS-aiding is not available, and the obtained navigation solution is poor. The Cumulative Distribution Functions (CDF) of the horizontal errors are provided in Figure 19.  
Figure 17: Number of tracked and LOS satellites over time. From Figure 19, considering the north direction, the TC GNSS/vision performs better than the GNSS-KF approach 82% of the time and 100% of the time when compared with the LC GNSS/vision. In the east direction, the proposed method performs better than the GNSS-KF 20% of the time and 99% of the time than the LC GNSS/vision. The performance of the proposed method over the LC GNSS/vision indicates the benefit of coupling the LOS satellites with the visual odometry. It can clearly be seen that when the GNSS PVT solution is not available (which happens 65.5% of the time and 100% of the time and 100% of the time), the proposed method performs better than the GNSS-KF 20% of the time and 99% of the time than the LC GNSS/vision. The performance of the proposed method over the LC GNSS/vision indicates the benefit of coupling the LOS satellites with the visual odometry. It can clearly be seen that when the GNSS PVT solution is not available (which happens 65.5% of the time and 100% of the time), the proposed method performs better than the GNSS-KF 20% of the time and 99% of the time than the LC GNSS/vision. Figure 19: Cumulative distribution functions of the horizontal errors. Figure 18: Comparison of position error characteristics between GNSS-KF and the proposed TC GNSS/vision. during the data collection), tight coupling of the small number of available GNSS observations is still able to improve the accuracy compared to using no GNSS observations (in the loose coupling case). The proposed method provides a significant improvement over the GNSS-only solution, demonstrating the benefit of fusing visual odometry with the GNSS solution. Returning back to Figure 16, Zone C depicts a straight moving zone where both TC GNSS/vision and GNSS-KF provide a poor position estimation. This case, although not alarming for the proposed method, requires further investigation.