Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments

• 发布时间：[2021-07-28] 来源：[学院] 点击量：[282]

作者: Li, T (Li, Tuan); Zhang, HP (Zhang, Hongping); Gao, ZZ (Gao, Zhouzheng); Niu, XJ (Niu, Xiaoji); El-sheimy, N (El-sheimy, Naser)

来源出版物: REMOTE SENSING  卷: 11  期: 6  文献号: 610  DOI: 10.3390/rs11060610  出版年: MAR 2 2019
摘要: Precise position, velocity, and attitude is essential for self-driving cars and unmanned aerial vehicles (UAVs). The integration of global navigation satellite system (GNSS) real-time kinematics (RTK) and inertial measurement units (IMUs) is able to provide high-accuracy navigation solutions in open-sky conditions, but the accuracy will be degraded severely in GNSS-challenged environments, especially integrated with the low-cost microelectromechanical system (MEMS) IMUs. In order to navigate in GNSS-denied environments, the visual-inertial system has been widely adopted due to its complementary characteristics, but it suffers from error accumulation. In this contribution, we tightly integrate the raw measurements from the single-frequency multi-GNSS RTK, MEMS-IMU, and monocular camera through the extended Kalman filter (EKF) to enhance the navigation performance in terms of accuracy, continuity, and availability. The visual measurement model from the well-known multistate constraint Kalman filter (MSCKF) is combined with the double-differenced GNSS measurement model to update the integration filter. A field vehicular experiment was carried out in GNSS-challenged environments to evaluate the performance of the proposed algorithm. Results indicate that both multi-GNSS and vision contribute significantly to the centimeter-level positioning availability in GNSS-challenged environments. Meanwhile, the velocity and attitude accuracy can be greatly improved by using the tightly-coupled multi-GNSS RTK/INS/Vision integration, especially for the yaw angle.