摘要
Central catadioptric cameras have been extensively adopted in robotics and surveillance due to their extensive field of view.To attain precise 3D information in these applications,it is important to calibrate the catadioptric cameras accurately.The existing calibration techniques either require prior knowledge of the mirror types,or highly depend on a conic estimation procedure,which might be ruined if there are only small portions of the conic visible on calibration images.In this paper,we design a novel planar pattern with concurrent lines as a calibration rig,which is more robust in conic estimation since the relationship among lines is taken into account.Based on the line properties,we propose a rough-to-fine approach suitable for the new planar pattern to calibrate central catadioptric cameras.This method divides the nonlinear optimization calibration problem into several linear sub-problems that are much more robust against noise.Our calibration method can estimate intrinsic parameters and the mirror parameter simultaneously and accurately,without a priori knowledge of the mirror type.The performance is demonstrated by both simulation and a real hyperbolic catadioptric imaging system.
Central catadioptric cameras have been extensively adopted in robotics and surveillance due to their extensive field of view. To attain precise 3D information in these applications, it is important to calibrate the catadioptric cameras accurately. The existing calibration techniques either require prior knowledge of the mirror types, or highly depend on a conic estimation proce- dure, which might be ruined if there are only small portions of the conic visible on calibration images. In this paper, we design a novel planar pattern with concurrent lines as a calibration rig, which is more robust in conic estimation since the relationship among lines is taken into account. Based on the line properties, we propose a rough-to-fine approach suitable for the new planar pattern to calibrate central catadioptric cameras. This method divides the nonlinear optimization calibration problem into several linear sub-problems that are much more robust against noise. Our calibration method can estimate intrinsic parameters and the mirror parameter simultaneously and accurately, without a priori knowledge of the mirror type. The performance is demonstrated by both simulation and a real hyperbolic catadioptric imaging system.
基金
Project (Nos. 60502006,60534070,and 90820306) supported by the National Natural Science Foundation of China
