In heavy-duty vehicles multiple signals are available to estimate the vehicle's kinematics, such as inertial, GPS and CAN linear and angular speed readings. These signals have different noise variance, bandwidth and sampling rate. In this paper we present a non-linear sensor fusion algorithm allowing asynchronous sampling and non-causal smoothing, and we apply it to study the accuracy improvements when incorporating CAN measurements to standard GPS+IMU kinematic estimation, as well as the robustness against missing data. Our results show that these asynchronous CAN+GPS+IMU sensor fusion is advantageous in low-speed manoeuvres and GPS-denial environments. Accuracy and robustness to missing data is of course improved with non-causal filtering. The proposed algorithm is based on Extended Kalman Filter and Smoother with exponential discretization of continuous-time stochastic differential equations, in order to process measurements at arbitrary time instants.
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