By Topic

UKF Sensor Data Fusion for Localisation of a Mobile Robot

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $33
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Matthias Baumann ; University of Würzburg, Institut of Computer Science VII, Germany ; Daniel Eck ; Laszlo Lemmer ; Klaus Schilling

The localisation of outdoor mobile robots is one of the most important challenges for implementing applications such as search and rescue, reconnaissance, surveillance and monitoring. The Global Positioning System (GPS) is a common used sensor system for localisation but the drawbacks of its limited accuracy are well known. These effects can cause mission failure especially for small sized mobile robots. To compensate these drawbacks, a sensor data fusion is introduced based on an Unscented Kalman Filter (UKF) that fuses GPS, inertial and incremental sensor data in an adaptive way. In case of GPS outages the typical INS drift can be avoided by a new alternative position update, which is calculated based on the last pose and the kinematic model that uses incremental encoder and yaw rate sensor data. The whole system is implemented on a low power micro controller.

Published in:

Robotics (ISR), 2010 41st International Symposium on and 2010 6th German Conference on Robotics (ROBOTIK)

Date of Conference:

7-9 June 2010