# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 6 Experiment and Results
### Results and Interpretations  
Our results will be evaluated by using the outputs of three integration approaches that will be compared with the reference trajectory:  
* The GNSS-only navigation solution: For this, the PLAN-nav (University of Calgary's module of the GSNRxTM software receiver) was used. As with most consumer-grade receivers, it uses local level position and velocity states for its GPS-only Kalman filter. In the results, this solution is referred to as GNSS-KF;
* The tightly-coupled GNSS/vision solution: The Line Of Sight (LOS) satellites are first selected. Then, this solution tightly couples the vision-based relative motion estimate to the GNSS. In the results, this is referred to as Tightly-Coupled (TC) GNSS/vision;
* The loosely-coupled GNSS/vision solution integrates measurements from the vision system with the GNSS least squares PVTSolution obtained by using range and range rate observations. Both systems independently compute the navigation solutions, and they are integrated in a loosely-coupled way. This means that if one of the system is unable to provide the solution (e.g., GNSS), then no update from that system is provided to the integration filter. This solution will help to clearly see how beneficial the proposed integration method is, especially when there are fewer than four (LOS) satellites. We refer to this as Loosely-Coupled (LC) GNSS/vision in the text. More details on integration strategies can be found in [10; 65]. In this work, only GPS L1 C/A pseudoranges and range rates are used. The entire path travelled during the data collection and the path considered for evaluating our proposed method are depicted in Figure 15 (in red and green, respectively, connecting Points 1, 2, 3, 4 and 5, as indicated by the arrows in Figure 14). The reason why we choose the green path for our experiment has been discussed in Section 6.1 and is shown in Figure 14.  
After the equipment calibration (in the circled area depicted in Figure 15), the vehicle stayed stationary for a few minutes before the experiment started. As observed in [42], at the beginning of the experiment, the initial heading of the GNSS/vision integrated solution can be very inaccurate due to \begin{table}
\begin{tabular}{l c} \hline \hline  & **Reference and GNSS** \\ \hline SPAN-SE & dual-frequency L1/L2 GPS + GLONASS \\ Combination & GPS + GLONASS combined with UIMU-LCI \\ \hline \multicolumn{3}{l}{**Camera (Lens Specification, Intrinsic Parameters) and Images**} \\ \hline Aperture & f/1.2—closed \\ Focal length & 2.2 mm \\ VFOV (1/3′′) & 90\% \\ Image resolution & 1288 \(\times\) 728 \\ Image frame rate & 10 fps \\ Image centre & (643.5, 363.5) \\ \(r_{1}\) & \(-\)0.26 \\ \(r_{2}\) & 0.08 \\ \(r_{3}\) & \(-\)0.01 \\ \(t_{1}\) & 0 \\ \(t_{2}\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data collection hardware specifications. VFOV, Vertical Field of View. the fact that the absolute heading is not available when the car is static. However, once the vehicle starts moving, the heading is quickly recovered. Figure 16 shows the reference (true) trajectory and the position estimation for GNSS-KF, Tightly-Coupled GNSS/vision (TC GNSS/vision), as well as the Loosely-Coupled GNSS/vision (LC GNSS/vision) integrated navigation. Figure 16: Estimated positions results. (**a**) Estimated position; (**b**) highlighted challenging zones; (**c**) zoomed challenging Zone (**d**) zoomed challenging Zone (**e**) zoomed challenging Zone (**f**) TC, Tightly-Coupled; LC, Loosely-Coupled. Figure 15: Reference path. Red: the entire travelled path when data were collected; green: the considered path in this paper. 1, 2, 3, 4, 5: portions of the travelled path before turns. From the plotted trajectory, it can be seen that the presented integration approaches closely follow the reference trajectory during the portion of the path where buildings are low (Path 1, shown in Figure 15).