## 1 Introduction
Nowadays, low-cost MEMS-based IMUs, video cameras and portable GNSS devices are commercially available for automotive applications and some manufacturers have already integrated such facilities into their vehicle systems. This research aims at robust ground vehicle navigation based on camera/IMU/GNSS integration against challenging GNSS environments where the blocked sky can significantly obstruct the view of low-elevation-angle satellites and hinder a user's navigation performance. Three key contributions are addressed herein: first, the proposed camera/IMU/GNSS integration architecture complements each of the sensors and potentially overcomes the disadvantage of traditional GNSS/IMU integration. For example, the number of line-of-sight (LOS) GNSS pseudoranges remains between two and three for a while such as wandering through the narrow streets in a city's downtown area. Although the tightly coupled GNSS/IMU integration allows the filter to obtain the user's position solution with sparse measurements, the overall solution is prone to divergence over time due to the lack of redundant constraints. On the other hand, with the aid of more than two LOS pseudorange observations from high-elevation-angle satellites, the baseline magnitude of position change within an allocated time interval can be extracted. This GNSS-derived baseline magnitude in turn enables a monocular camera to resolve its scale factor ambiguity if the data rates of GNSS and camera sensors are synchronized. Second, although significant effort has been spent on developing visual odometry algorithms, the efficiency and performance regarding image frame rate has yet to be evaluated. The navigation information derived from the computer vision algorithms is, ideally, independent of frame rate, provided two adjacent image frames overlap each other. However, in reality the actual situation does not provide this independence due to the vehicle dynamics and sufficiency of the accurate image feature correspondences. In an effort to balance the tradeoff between practical performance and computational cost, we deliberately reduce the image frame rate and examine the positioning accuracy of our integrated scheme. Third, the integrated system is validated through a live dataset which was collected in an operational traffic environment with available centimeter-accuracy truth reference. Although there are previous usable datasets for SLAM-based analysis, few of them were collected in the actual environment with available high-accuracy truth reference [13, 14]. The camera images we used were captured in an operational traffic environment with vehicles passing by and overtaking the host vehicle during the test period. The high-accuracy truth reference was generated by leveraging three RTK GPS receivers on the vehicle roof top, which facilitates a fair and reliable comparison between algorithms under assessment. The only simulation in this paper is the GNSS pseudorange measurements. The remainder of the paper proceeds as follows: we first introduce the coordinate frames utilized in this paper. The inertial navigation and its error model are then summarized. Thirdly, the principle of computer vision-based motion estimation is presented. GNSS simulation and differential processing are discussed afterwards. Based on the above sensor description, the proposed EKF model and integrated architecture are established, followed by the live test validation. The paper concludes with the results as well as planned future work.