Parameter Estimation of an Industrial Car-Like Tractor

Abstract

This letter presents a practical method for parameter estimation of a full-size industrial car-like tractor. With only on-vehicle sensors including three encoders, an IMU and a 3D LiDAR, we estimate the effective radius of rear wheels, ratio between the steering wheel angle and road wheel angle, the longitudinal position of center of gravity (COG), the yaw moment of inertia and the longitudinal and cornering tire stiffnesses. This letter innovatively introduces nonlinear vehicle-dynamics constraints into a factor-graph estimation framework that also fuses IMU and LiDAR measurements, thus achieving an easy-to-tune and highly-accurate parameter estimation solution. The batch maximum a posterior (MAP) problem is formulated and solved efficiently for both states and parameters. To make it even easier to tune, we perform specifically-designed motion patterns to simplify the required description model, and we estimate as fewer parameters as possible for one motion pattern. The dynamic constraints are also formulated according to the motion properties. Performance and results of the proposed method are validated in detail with experiments on the industrial car-like tractor.