This paper presents a method to perform Euclidean calibration on camera and projector-based structured light systems, without assuming specific scene structure. The vast majority of the methods in the literature rely on prior knowledge of the 3D scene geometry in order to perform calibration, i.e., the nature of the occluding bodies in the scene needs to be known beforehand in order to calibrate structured light systems. Examples of prior knowledge used include using known stationary occluding bodies, precisely maneuvering known occluding bodies, knowing the exact world location of projected points or lines, or ensuring the entire scene obeys some other specific setup. By using multiple cameras, the method presented in this paper is able to calibrate camera and projector systems without requiring any of these constraints on occluding bodies in the scene. The method presented optimizes the calibration of the scene in terms of image-based reprojection error. Simulations are shown which characterize the effect noise has on the system, and experimental verification is performed on complex and cluttered scenes. The main contribution of this paper is the elimination of the requirement of using known occluding bodies in the scene for camera and projector-based structured light system calibration, which has not been extensively studied.
|Original language||English (US)|
|Number of pages||9|
|Journal||IEEE Transactions on Automation Science and Engineering|
|State||Published - Oct 2011|
Bibliographical noteFunding Information:
Manuscript received March 17, 2010; revised September 25, 2010, February 17, 2011; accepted March 27, 2011. Date of publication May 23, 2011; date of current version October 05, 2011. This paper was recommended for publication by Associate Editor J. Xiao and Editor S. Sarma upon evaluation of the reviewers’ comments. This work was supported in part by the National Science Foundation under Grant CNS-0821474, Grant IIP-0934327, Grant CNS-1039741, and Grant SMA-1028076, in part by the Medical Devices Center, University of Minnesota, and in part by the Digital Technology Center, University of Minnesota.
- medical vision
- structured light