DocumentCode :
2534111
Title :
A novel multi-planar LIDAR and computer vision calibration procedure using 2D patterns for automated navigation
Author :
Huang, Lili ; Barth, Matthew
Author_Institution :
Dept. of Electr. Eng., Univ. of California, Riverside, CA, USA
fYear :
2009
fDate :
3-5 June 2009
Firstpage :
117
Lastpage :
122
Abstract :
In this research, we propose a unique multi-planar LIDAR and computer vision calibration algorithm. This method only requires the camera and LIDAR to observe a planar pattern at different positions and orientations. Geometric constraints of the dasiaviewspsila from the LIDAR and camera images are resolved as the coordinate transformation coefficients. The proposed approach consists of two stages: solving a closed-form equation, followed by applying a non-linear algorithm based on a maximum likelihood criterion. To the author´s best knowledge, this is the first paper for a multi-planar LIDAR and vision system calibration. Compared with the classical methods which use dasiabeam-visiblepsila cameras or 3D LIDAR systems, this approach is easy to implement at low cost. Additionally, computer simulation and real world testing have been carried out to evaluate the performance of this approach. Lastly, application of the technique for automated navigation is presented.
Keywords :
automated highways; cameras; computer vision; matrix algebra; navigation; optical radar; vehicles; 2D pattern; automated navigation; camera; closed-form equation; computer simulation; computer vision calibration; coordinate transformation coefficient; maximum likelihood criterion; multiplanar LIDAR; nonlinear algorithm; Calibration; Cameras; Computer simulation; Computer vision; Costs; Image resolution; Laser radar; Machine vision; Navigation; Nonlinear equations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium, 2009 IEEE
Conference_Location :
Xi´an
ISSN :
1931-0587
Print_ISBN :
978-1-4244-3503-6
Electronic_ISBN :
1931-0587
Type :
conf
DOI :
10.1109/IVS.2009.5164263
Filename :
5164263
Link To Document :
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