GNSS/MEMS IMU vehicle integrated navigation algorithm constrained by displacement vectors in urban environment

Abstract The global navigation satellite system (GNSS) and a micro-electro-mechanical system inertial measurement unit (MEMS IMU) can be integrated to form a robust navigation system, providing continuous and accurate location information on dynamic vehicles. However, the performance of a GNSS/MEMS IMU integrated navigation system is easily affected by the accuracy of measurement noise matrix, which is significantly reduced in the urban environment. The adaptive Kalman filter can improve the measurement noise matrix reliability by constructing robust equivalent weight factors; however, GNSS observations are easily misjudged as exception sources that can deteriorate the filter stability. To address these problems, a novel integrated algorithm with a displacement vector constraint is proposed based on the kinematic characteristics of vehicles, which are considered as judgment factors. In this case, the measurement noise can be adjusted accordingly only when the GNSS observations are judged as abnormal. In this study, two types of vehicle navigation experiments were conducted: a low-speed wheeled robot in a campus environment and a dynamic vehicle in an actual urban environment. The experimental results showed that the three-dimensional navigation accuracy was improved by 47% and 55% compared with the conventional algorithm under the two different environments, respectively.