## 7 Experimental Description and Result Analysis
In the _Campus_0L_ segment, the rectified image frames are utilized instead of the originally captured ones to avoid the image projection offset and obtain accurate motion estimation parameters for sensor integration. Although the \(b\) and \(c\) frames do not necessarily coincide with each other as in our case, the camera and IMU sensors are usually rigidly fixed to the vehicle and their relative translation and rotation parameters are, therefore, constant. The rotation calibration between the \(b\) and \(c\) frames can be accomplished according to Lobo [38] while the lever arm vector needs more effort to be accurately estimated. The difficulties of lever arm vector estimation arise due to the sensitivity of the selected target position and the geometric configuration [38]. Normally, in low dynamic situations with moderate maneuvers, particularly for ground vehicle applications, a mild lever arm has little impact little on the navigation solution and it does not necessarily require an accurate estimation [15,39]. Therefore, although both rotation and translation calibration results between the \(b\) and \(c\) frames are provided in the dataset, we simply disregard the lever arm effect caused by the translation and take only the relative rotation _C\({}_{c}^{b}\)_ into consideration. As described in Section 5, the scale factor ambiguity between every two successive image frames can be resolved using GNSS differential techniques, capable of obtaining the distance traveled over ground. In an effort to acquire more available GNSS measurements in potentially challenging environments, we take advantage of both GPS and GLOASS constellations for the pseudorange simulation. In typical challenging GNSS environments like urban downtown areas, low-elevation-angle signals are prone to severe multipath degradation or obstruction caused by nearby buildings. We, therefore, filter out those low-elevation-angle satellites from subsequent integration demonstrations. Figure 6 shows the sky plot of GPS/GLONASS satellites when the dataset was initially collected in Malaga with the elevation mask angle of 40 degree. The GPS pseudo random number (PRN) is between 1 and 32, while GLONASS orbital slot is designated within the range 51 to 74. Although the elevation mask angle is significantly higher than in most other applications, both constellations  
Figure 6: Sky plot of available GPS and GLONASS satellites with the elevation mask angle of 40 degree. contributed 8 space vehicles for the integration processing. According to the differential operation described in Equation (15), most of the common error sources can be eliminated from the satellite-to-user geometry. It is simulated in this paper that the LOS pseudorange measurements contain thermal noise with 0.25 m root-mean-square error (RMSE) during the whole _Campus_0L_ segment. Note that the two constellations do not share the same coordinate system as well as time reference. In order to avoid newly introduced unknown quantities, the transformation between their coordinate frames has been implemented by using the approach presented by [PERSON] [40] and, similarly, the system time offset can be extracted from the ephemerides of all modernized GLONASS-M satellites [41]. All the camera, IMU and GNSS measurements are timestamped. Instead of using the original 7.5 Hz frame rate, we deliberately downsample the visual measurements to 2.5 Hz to reduce the computational load and maintain preferable alignment with the 100 Hz IMU measurements for the EKF state update estimation. Since the IMU measurements were not necessarily synchronized with the image frames, an extrapolation process of IMU measurements is expected to establish synchronization consistency. According to [PERSON] [42], a second-order polynomial extrapolation is utilized to balance the computational load and the accuracy of extrapolation.