## 1 Introduction  
GNSS systems have been the prominent technology to fulfill the demanding requirements for road navigation. However, GNSS signals are subject to attenuation, reflections, or even blockages as a result of driving in difficult signal reception areas, such as urban canyons, enclosed parking garages and underground concrete tunnels. RF interference (RFI), whether deliberate or not, can misguide the user towards an erroneous position solution [1]. The use of augmentation technologies is capable of mitigating these disadvantages of standalone GNSS solutions and providing improved availability and accuracy. By coupling the measurements from both GNSS and IMU sensors, integration techniques are commercially employed to calculate a high-bandwidth navigation solution. This effectively enables the calibration of the IMU sensors and the control of the error growth of inertial navigation, and also enables higher data output compared to a standalone GNSS solution. A number of researchers have presented detailed realization methods and performance analyses of the loosely/tightly coupled GNSS/IMU implementations [2; 3; 4]. Apart from the traditional navigation technologies, the progress in computer vision has motivated a variety of location-based applications, such as simultaneous localization and mapping (SLAM) [5], and provided potential capabilities for the navigation community. Visual sensors usually incorporate other sensors, such as GNSS, IMU and laser scanners, to achieve a system level integration. [PERSON] [6] provided an integration of GPS carrier phase and vision-based measurements for navigation in GPS challenged environments. The GPS carrier phase measurements were used to resolve the scale ambiguity of the unknown change in position vector. The algorithm was validated via simulations as well as live experiments. In more common cases, a vision sensor is incorporated with an IMU sensor for motion estimation with high frequency. [PERSON] [7] presented a tutorial overview on inertial and visual sensors and gave the essential principles of integrating them to obtain robust estimates of sensor motion and 3D reconstruction. The integration strategy is categorized into the loose and tight coupling. A detailed performance comparison of the loosely/tightly coupled camera/IMU integration was given by [PERSON] [8]. The tightly coupled integration might yield higher accuracy, but it is prone to divergence under certain conditions and _vice versa_. By utilizing strapdown inertial sensors and a fisheye lens, [PERSON] [9] demonstrated a navigation system for an indoor vertical-take-off-and-landing (VTOL) micro aerial vehicle (MAV) based on the Kalman filter. The inverse depth method was applied to determine the movement of the camera. The scale factor ambiguity was recovered by simulating the laser ranging as additional source of information and measuring the flight height over ground. Apart from using a monocular camera, [PERSON] [10] proposed a multi-aperture approach integrated with IMU measurements. By observing the feature points over a wide range, the vision-related navigation uncertainty could be significantlyreduced. The unit sphere on which the feature points were mapped was presented and used to model the sensor fusion algorithm. As opposed to utilizing a forward-looking camera, [PERSON] [11] used a camera with a ground-facing orientation to accomplish the camera/IMU integration for pedestrian navigation. Given the estimated height to the ground, the camera velocity was retrieved from the translation information. The residual between IMU and camera velocities then served as the Kalman filter's measurements. With a similar IMU/camera mounting method, [PERSON] [12] provided a tightly coupled integration by using the residual between the predicted and actual feature location as the filter's input, which was expressed in the image coordinate frame.