Odometry Error Estimation for a Differential Drive Robot Snowplow

E.J. Kreinar, R.D. Quinn

Abstract: This paper presents a velocity-augmented Extended Kalman Filter (EKF) which can estimate both systematic and non-systematic odometry errors for a differential drive mobile robot. The proposed EKF is validated both within simulation and using post processed robot snowplow data from the Institute of Navigation’s 2013 Autonomous Snowplow Competition. Potential sensor configurations are explored using EKF Monte-Carlo simulations with Global Positioning Systems (GPS) sensors or multilateration ranging sensors.
Published in: Proceedings of IEEE/ION PLANS 2014
May 5 - 8, 2014
Hyatt Regency Hotel
Monterey, CA
Pages: 1122 - 1129
Cite this article: Kreinar, E.J., Quinn, R.D., "Odometry Error Estimation for a Differential Drive Robot Snowplow," Proceedings of IEEE/ION PLANS 2014, Monterey, CA, May 2014, pp. 1122-1129.
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