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Four-Wheeled Dead-Reckoning Model Calibration using RTS Smoothing


Localization is one of the main challenges to be addressed to develop autonomous vehicles able to perform complex maneuvers on roads opened to public traffic. Having an accurate dead-reckoning system is an essential step to reach this objective. This paper presents a dead-reckoning model for car-like vehicles that performs the data fusion of complementary and redundant sensors: wheel encoders, yaw rate gyro and steering wheel measurements. In order to get an accurate dead-reckoning system with a drift reduced to the minimum, the parameters have to be well calibrated and the procedure has to be simple and efficient. We present a method able to accurately calibrate the parameters without knowing the ground truth by using a Rauch-Tung-Striebel smoothing scheme which enables to obtain state estimates as close to the ground truth as possible. The smoothed estimates are then used within a optimization process to calibrate the model parameters. The method has been tested using data recorded from an experimental vehicle on public roads. The results show a significant diminution of the dead-reckoning drift compared to a commonly used calibration method. We evaluate finally the average distance a vehicle can navigate without exteroceptive sensors by using the proposed four-wheeled dead reckoning system.
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hal-02157572 , version 1 (17-06-2019)



Anthony Welte, Philippe Xu, Philippe Bonnifait. Four-Wheeled Dead-Reckoning Model Calibration using RTS Smoothing. IEEE International Conference on Robotics and Automation (ICRA 2019), May 2019, Montreal, Canada. pp.312--318, ⟨10.1109/ICRA.2019.8794270⟩. ⟨hal-02157572⟩
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