Online Calibration of Vehicle Powertrain and Pose Estimation Parameters using Integrated Dynamics

Neal Seegmiller, Forrest Rogers-Marcovitz, and Alonzo Kelly
IEEE International Conference on Robotics and Automation, May, 2012.


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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.

Keywords
model identification, calibration, wheeled mobile robots, powertrain, position estimation, odometry

Notes
Associated Center(s) / Consortia: National Robotics Engineering Center

Text Reference
Neal Seegmiller, Forrest Rogers-Marcovitz, and Alonzo Kelly, "Online Calibration of Vehicle Powertrain and Pose Estimation Parameters using Integrated Dynamics," IEEE International Conference on Robotics and Automation, May, 2012.

BibTeX Reference
@inproceedings{Seegmiller_2012_7065,
   author = "Neal Seegmiller and Forrest Rogers-Marcovitz and Alonzo Kelly",
   title = "Online Calibration of Vehicle Powertrain and Pose Estimation Parameters using Integrated Dynamics",
   booktitle = "IEEE International Conference on Robotics and Automation",
   month = "May",
   year = "2012",
}