DocumentCode
1776113
Title
Ground-truth localization using a sequential-update extended Kalman filter
Author
Zeino, Eyad ; Paulik, Mark ; Krishnan, Mohan ; Luo, Cheng ; Overholt, James ; Hudas, Greg ; Udvare, Thomas
Author_Institution
Dept. of Electr. & Comput. Eng., Univ. of Detroit Mercy, Detroit, MI, USA
fYear
2014
fDate
5-7 June 2014
Firstpage
103
Lastpage
108
Abstract
A ground-truth environment for mobile robot localization is developed. The sequential update extended Kalman filter is employed to fuse data from a Sick Nav200 laser positioning system and multiple onboard sensors to provide highly accurate robot pose estimation. Results are suitable for validation of mapping, localization and kino-dynamic modeling. Experimental work presented covers odometry calibration and system validation.
Keywords
Kalman filters; mobile robots; path planning; pose estimation; robot vision; sensors; Sick Nav200 laser positioning system; ground-truth localization; kino-dynamic modeling; mobile robot localization; odometry calibration; onboard sensors; robot pose estimation; sequential-update extended Kalman filter; Compass; Equations; Kalman filters; Mathematical model; Robots; Sensors; Vehicles; Extended Kalman Filter; Localization; Multi-Sensor Fusion; Odometry Calibration; Sequential Sensor Fusion;
fLanguage
English
Publisher
ieee
Conference_Titel
Electro/Information Technology (EIT), 2014 IEEE International Conference on
Conference_Location
Milwaukee, WI
Type
conf
DOI
10.1109/EIT.2014.6871747
Filename
6871747
Link To Document