SEAMLESS REALTIME LANE LEVEL VEHICULAR NAVIGATION IN GNSS CHALLENGING ENVIRONMENTS USING A RTK GNSS/IMU/VINS INTEGRATION SCHEME
Keywords: Global Navigation Satellite System, Inertial Navigation System, Sensor Integration, GNSS High-Challenge, Visual Odometry
Abstract. Outdoor positioning requires a reliable solution that can work in environments where satellite signals are often blocked or degraded. Global Navigation Satellite System (GNSS) is a common choice, but it may not provide accurate results for land vehicles. To address this challenge, this research proposes a multi-sensor integrated system for vehicle navigation that combines GNSS with other sensors. The system uses Extended Kalman Filter (EKF) to fuse the data from different sources and improve the navigation performance. The algorithm targets to provide seamless navigation for urban environments as well as various indoor environments fields with INS/GNSS/VIO aiding integrated solutions. The experimental vehicle of this research is equipped with a tactical-grade inertial sensing measurement unit (IMU) as the test system, a self-designed and assembled visual platform, which includes a camera with a time synchronization protocol and a low-cost IMU. Also, both indoor experimental fields and outdoor urban scenarios with different high challenging were tested to verify the developed algorithm. To evaluate the performance of the proposed real-time navigation system, we use a high-accuracy navigation-grade system as a reference, which provides a stable and reliable trajectory. The result indicates that using the GNSS RTK solution with VIO aiding integration scheme reduced the RMS errors in long outage (450 sec, 1812 m) by 87.4% and 79.9% in position and velocity error, respectively. In urban scenario, the along-track/cross-track maximum errors can achieve 1.4 m / 1.5 m. Overall, these contribute to the development of real-time navigation systems for self-driving vehicle in the future.