Odometry error estimation for a differential drive robot snowplow

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 postprocessed 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 System (GPS) sensors or multilateration ranging sensors.