Estimation of Road Slope Based on IMU Error Compensation and GPS Based Multi-Sensor Fusion

In the road test of vehicle performance evaluation, real-time and accurate estimation of road slope is essential for objective evaluation. Using slope meter directly can bring many problems such as large randomness and errors in road test. Using complex road slope estimation algorithm often brings redundant sensors and reduces detection efficiency. Aiming at the above problems, this paper proposes a road slope estimation model based on IMU error calibration and multi-sensor signal fusion. First, the vehicle-road dynamics and kinematics models are established. Then, the error sources of IMU are analyzed, and the calibration and compensation methods are proposed. The acceleration signal of IMU is compensated by inertia through the vehicle velocity signal obtained by multi-sensors, and the projection of gravity acceleration vector in the vehicle coordinate is decoupled. Finally, the model fuses the decoupled result with IMU angular velocity value through Kalman filter algorithm, and outputs the estimated slope of the road. The road test results show that the model can effectively compensate the IMU installation error and accurately estimate the road slope. And the slope estimation error is less than 0.5%, which can meet the needs of the road test of vehicle performance evaluation.