Integrated navigation accuracy improvement algorithm based on multi-sensor fusion

Abstract

In recent years, autonomous driving technology has risen. Accurate positioning is an important part of self-driving cars. Global positioning system (GPS) and inertial navigation system (INS) are two commonly used navigation systems. Because GPS signals are easy to be interfered and IMU is easy to accumulate errors, they usually overcome each other’s shortcomings through Kalman filtering. The error model of the two systems is established. And add a speedometer to the system. Using extended Kalman filter (EKF) to build GPS/INS/ odometer/integrated navigation system is a practical data fusion method. This paper proposes a GPS/INS/ odometer integrated navigation system, which can provide accurate location and azimuth information. It meets the requirements of autonomous vehicle navigation. The experimental results show that the odometer can effectively assist the IMU even when the GPS signal is not available, and the algorithm is stable and reliable. The feasibility and effectiveness of the algorithm are verified by field test.