Multi-modal sensor Health Aids fusion has become ubiquitous in the field of vehicle motion estimation.Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed.In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation.
The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU).Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases.The estimation problem is treated as a joint state and Homeopathic parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems.
Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems.The effectiveness of the proposed calibration procedure is demonstrated using real sensor data.The results show that estimation accuracies below 0.
1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.