DocumentCode
174876
Title
Odometry error estimation for a differential drive robot snowplow
Author
Kreinar, Edward J. ; Quinn, Roger D.
fYear
2014
fDate
5-8 May 2014
Firstpage
1122
Lastpage
1129
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 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.
Keywords
Kalman filters; distance measurement; mobile robots; nonlinear filters; EKF; EKF Monte-Carlo simulations; GPS sensors; Global Positioning System; differential drive mobile robot; differential drive robot snowplow; multilateration ranging sensors; nonsystematic odometry errors; odometry error estimation; postprocessed robot snowplow data; sensor configurations; systematic odometry errors; velocity-augmented extended Kalman filter; Measurement uncertainty; Mobile robots; Robot sensing systems; Systematics; Velocity measurement; Wheels;
fLanguage
English
Publisher
ieee
Conference_Titel
Position, Location and Navigation Symposium - PLANS 2014, 2014 IEEE/ION
Conference_Location
Monterey, CA
Print_ISBN
978-1-4799-3319-8
Type
conf
DOI
10.1109/PLANS.2014.6851482
Filename
6851482
Link To Document