Asynchronous Sensor Fusion of GPS, IMU and CAN-Based Odometry for Heavy-Duty Vehicles

In heavy-duty vehicles, multiple signals are available to estimate the vehicle's kinematics, such as Inertial Measurement Unit (IMU), Global Positioning System (GPS) and linear angular speed readings from wheel tachometers on internal Controller Area Network (CAN). These have different noise variance, bandwidth sampling rate (being latter, possibly, irregular). this paper we present a non-linear sensor fusion algorithm allowing asynchronous non-causal smoothing. It is applied achieve accuracy improvements when incorporating odometry measurements CAN bus standard GPS+IMU kinematic estimation, well robustness against missing data. Our results show that multi-sensor (GPS+IMU+CAN-based odometry) advantageous in low-speed manoeuvres, improving data, thanks filtering. The proposed based Extended Kalman Filter Smoother, with exponential discretization of continuous-time stochastic differential equations, order process at arbitrary time instants; it can provide data subsequent processing steps instants, not necessarily coincident original measurement ones. Given extra information smoothing case, its estimation performance less sensitive noise-variance parameter setting, compared causal Working Matlab code provided end work.