DocumentCode
3527887
Title
Sensor fusion-based line detection for unmanned navigation
Author
Chun, Changmook ; Suh, SeungBeum ; Roh, Chi-won ; Kang, Yeonsik ; Kang, Sungchul ; Lee, Jung-yup ; Han, Chang-Soo
Author_Institution
Cognitive Robot. Center, Korea Inst. of Sci. & Technol. (KIST), South Korea
fYear
2010
fDate
21-24 June 2010
Firstpage
191
Lastpage
196
Abstract
We propose an algorithm of reliable detection of line for unmanned navigation of mobile robots using sensor fusion. To detect the distance and the angle between the robot and the line, we use a vision sensor system and a laser range finder (LRF). Each sensor system runs its own extended Kalman filter (EKF) to estimate the distance and orientation of the line. The vision system processes images being captured using well-known edge detection algorithms, and the LRF detects the line using the measurement of the intensity of the laser beam reflected. However, depending on the condition of the road and ambient light, each sensor gives us wrong measurement of the line or sometimes completely fails to detect it. To resolve such uncertainty, we develop a simple and easy-to-implement sensor fusion algorithm that uses weighted sum of the output of each EKF, and it gives us more reliable estimate of the distance and orientation of the line than each measurement/estimator system.
Keywords
Kalman filters; edge detection; image fusion; laser ranging; mobile robots; robot vision; EKF; LRF; ambient light; distance estimation; edge detection algorithms; extended Kalman filter; images processing; laser beam intensity measurement; laser range finder; mobile robots; sensor fusion-based line detection; unmanned navigation; vision sensor system; Image edge detection; Laser beams; Laser fusion; Machine vision; Mobile robots; Navigation; Robot sensing systems; Robot vision systems; Sensor fusion; Sensor systems; Autonomous navigation; Extended kalman filter; Intensity of laser reflected; Line detection; Sensor fusion; Vision;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Vehicles Symposium (IV), 2010 IEEE
Conference_Location
San Diego, CA
ISSN
1931-0587
Print_ISBN
978-1-4244-7866-8
Type
conf
DOI
10.1109/IVS.2010.5547995
Filename
5547995
Link To Document