Experiment of vehicle localization based on polynomial Kalman filter

In order to study the application of polynomial Kalman filter(PKF) in the vehicle location, and to analyze state estimation accuracy of PKF affected by the order of polynomial and evaluate the performance of the PKF, in this paper, by adopting polynomial fitting method in system model of kalman filter to model nonlinear system, three PKFs were established and applied for vehicle localizaiton experiment with mulitiple sensors fusion. Firstly, previous researches on PKF in tracking accuracy affected by the order of polynomial and in performance evaluation were introduced. Then, zero-order, first-order and second-order PKF were establish using corresponding order of polynomial to fit the longitudinal velocity and heading of encoder dead-reckon model. Experiment was conducted on Pioneer3-AT moble robot platform with Encoder and AHRS used as sensor data input, measurement of RTK-GPS was as reference trajectory. Also, the theorectical error and actual error of the PKF were compared to evaluate the performance of the three PKFs. The experiment result showed that the actual error of the three PKFs were within the theorectical error bounds in more than 68% filtering time which indicated normal status of the filters. The localization accuracy of zero-order PKF were increased by 63% and 73% in X, Y axis respectively compared with encoder dead-reckon method. Localization accuracy of first-order PKF was better than zero-order, and second-order was better than zero-order but worse than first-order, which showed that polynomial fitting the longitudinal velocity and heading of encoder dead-reckon model using higher order could not contribute to even better localization accuracy. This paper provides references for the construction and performance evaluation of PKF, as well as its practical implementation on vehicle localization.