Measurement Quality Control Aided Multisensor System for Improved Vehicle Navigation in Urban Areas

Integration of the global positioning system (GPS) with other sensors to improve positioning and navigation is a viable solution for intelligent vehicles. In particular, integrating GPS with an inertial measurement unit (IMU) and vision sensor is a common consideration for enhancing the performance of vehicle navigation systems. However, the accuracy and availability of GPS are seriously degraded by nonline-of-sight (NLOS) reception and multipath interference in urban environments. Combining the unreliable GPS positioning results with other sensors will degrade the performance of the integrated navigation system. Therefore, we propose a novel measurement quality control algorithm for a loosely coupled GPS, IMU, and stereo visual odometry (VO) integration for urban vehicle navigation. In the proposed algorithm, an adaptive fusion strategy based on geometric dilution of precision and a measurement fault detection and exclusion based on the K-means clustering considering predicted pseudorange errors are employed to mitigate NLOS/multipath effects. The validation of the proposed integrated solution incorporating the measurement quality control is carried out. The results show that the algorithm achieves positioning accuracy in terms of three-dimensional root mean square error of 2.85 m and 7.17 m, in mid and deep urban canyons, respectively. This level of performance is superior to that of the traditional GPS/IMU/VO integration. The corresponding improvements are 49.0% in the mid urban canyon, and 79.6% in the deep urban canyon, respectively.