This paper proposes a novel approach that performs extrinsic parameter estimation of a camera installed in a man-made environment using a single image. The problem of extrinsic parameter calibration is identical to 6DoF (six-degrees of freedom) localization problem of the camera. We take advantage of line information that is usually present in the man-made environment such as inside of the building. Our approach only requires a flat surface map for a 3D environment model which can be easily obtained from the blueprint of the artificial environment (e.g., CAD data). In order to manage the complicated 6DoF search problem, we propose a novel image descriptor defined in quantized Hough space to perform 3D-2D matching process between line features from the 3D flat surface model and the 2D single image. The proposed method can robustly estimate the complete extrinsic parameters of the camera, as we demonstrate experimentally.
[1]
Leonidas J. Guibas,et al.
A metric for distributions with applications to image databases
,
1998,
Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[2]
Hiroshi Mizoguchi,et al.
Robotic room: Symbiosis with human through behavior media
,
1996,
Robotics Auton. Syst..
[3]
C. Guestrin,et al.
Distributed localization of networked cameras
,
2006,
2006 5th International Conference on Information Processing in Sensor Networks.
[4]
Atsushi Yamashita,et al.
Automatic calibration of camera sensor networks based on 3D texture map information
,
2017,
Robotics Auton. Syst..
[5]
Joo-Ho Lee,et al.
Intelligent Space — concept and contents
,
2002,
Adv. Robotics.
[6]
Guannan Gao,et al.
Probabilistic Hough Transform
,
2011
.