Online Calibration of Vehicle Powertrain and Pose Estimation Parameters using Integrated Dynamics
Abstract
This paper presents an online approach to calibrating vehicle model parameters that uses the integrated dynamics of the system. Specifically, we describe the identification of the time constant and delay in a first-order model of the vehicle powertrain, as well as parameters required for pose estimation (including position offsets for the inertial measurement unit, steer angle sensor parameters, and wheel radius). Our approach does not require differentiation of state measurements like classical techniques; making it ideal when only low-frequency measurements are available. Experimental results on the LandTamer and Zoe rover platforms show online calibration using integrated dynamics to be fast and more accurate than both manual and classical calibration methods.
BibTeX
@conference{Seegmiller-2012-7476,author = {Neal Seegmiller and Forrest Rogers-Marcovitz and Alonzo Kelly},
title = {Online Calibration of Vehicle Powertrain and Pose Estimation Parameters using Integrated Dynamics},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {2012},
month = {May},
pages = {3969 - 3974},
keywords = {model identification, calibration, wheeled mobile robots, powertrain, position estimation, odometry},
}