Quaternion-based IMU and stochastic error modeling for intelligent vehicles

This paper focuses on the development of an IMU measurement simulator for navigation estimation algorithms validation. Its aim is to generate the sensor measurements thanks to an input trajectory described by the position and the orientation. The proposed models are derived from an inverse kinematic modeling of the sensors and an identification of their stochastic errors. These latter are composed of the biases instability, random walks and finally the sensors dynamics and bandwidth. The error model parameters of a low cost MEMS-IMU are determined using the Allan Variance method. In a second step, a Matlab simulator is built gathering the aforementioned models. Thanks to their completeness, this simulation tool is characterized by its wide range of application fields and dynamics that can be described. Its aim is to determine, from the time-dependent position and orientation data, the IMU measurements (3D accelerations and angular rates) without any object model. Finally, the simulator is validated using real experiments performed with an instrumented test car in normal driving as well as in obstacle avoidance situations.