# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 6 Experiment and Results
### Results and Interpretations
The two reasons mentioned previously that cause poor integrated solutions occur at this location. Due to a traffic jam, the vehicle stopped under a bridge where LOS satellites were not observed, and the visual odometry was very poor, caused by the fact that very few feature points were detected (the very dark captured images are shown in Figure 20b). In this situation a GNSS/vision/INS integrated system, for example (or GNSS/vision/\"appropriate sensor\"), would be beneficial. The GPS constellation at this time is shown in Figure 21a. Due to the camera's FOV, the high elevation satellites that appear to be obstructed by the bridge are in fact tracked, but are excluded from both solutions. This is shown in Figure 21b. The remaining satellites that fall out of the image are considered as obstructed. From the number of the tracked and LOS satellites over time depicted in Figure 17, it can be seen that when the vehicle is stopped under the bridge, although a few satellites were still tracked, no LOS was observed (see Figures 22b and 17, Epochs 628 to 684). To see the performance in terms of the reliability, the residuals of the satellites are shown in Figure 22.  
These represent the difference between the actual and the solved measurements and thus indicate the level of agreement of the observations. In open-sky (during the first 100 s), the innovation values are bounded between \(\pm\)5 m. In the challenging zone, they are larger. The end circle markers in Figure 22 indicate values in excess of \(\pm\)30 m. By applying statistical testing, satellites with larger innovation values, i.e., the outliers, are detected, and their measurements are prevented from corrupting the solution in the GNSS-KF case. For example, at Epoch 413179 (Figure 21), PRN 19 has a high elevation, but has very large innovation values (Figure 22); thus, its measurements are not used in the GNSS-only case. Even if not the case, our NLOS mitigation method had discarded this specific PRN since the projected satellite falls out of the image \"scope\" and is considered as obstructed. However, in some cases, satellites with small innovation values can be obstructed and thus degrade the final position solution. PRN17 is a good example of this. PRN17 has a very high elevation and small innovation values, most of the time. Its measurements are thus trusted and used in the GNSS-KF even if obstructed (\"slightly\" degrading the solution). In this case, the NLOS mitigation algorithm is able to determine this PRN as obstructed, and its measurements are not used, leading to an improved solution. Throughout the test, this occurs with other high satellites that are briefly obstructed. Figure 20: Bridge causing position errors. Surprisingly, as shown in Figure 18, during the first 100 s, the GNSS-KF strategy outperforms the TC GNSS/vision approach in the east direction. The obvious reason behind this is the poor vision performance during this time span (that was close to an open-sky scenario: one side of the road  
Figure 21: Sky plot of the GPS constellation at Epoch 413179 and its reception conditions. Figure 16:0 GNSS-only PR residuals. unobstructed, thus, less and bad feature point distribution). However, once the receiver's antenna is obstructed, the TC GNSS/vision approach becomes more accurate than GNSS-KF. The statistics of the 2D position errors of the considered path are shown in Figure 16:1  
These statistics are comparable or better than the results obtained in [29] for urban canyon tightly-coupled GNSS/vision (with similar equipment) where a stereo camera system is used instead. This is expected because NLOS satellites are not rejected in this work, which degrades their solution compared to the method presented in this paper.