摘要
针对折反射全方位视觉系统装配时需针对单视点约束进行精确校准的特点,提出了一种基于光学畸变校正的折反射全方位视觉系统单视点约束测定方法.首先采用神经网络建立实际图像和理想透视图像的复杂映射关系,获得可用于单视点约束测定的理想透视模型;然后根据空间圆透视投影特性,利用反射镜上端面边缘图像估计出反射镜相对于相机的位置和姿态,以此指导系统单视点约束测定.由于反射镜位置与姿态的估计是基于实际的成像模型推算的,因此降低了单纯依赖简化模型带来的估计误差,从而提高了测定精度,具有较强的实用性.实验利用仿真图像和真实图像共同验证了该测定方法的精确度和有效性.
The single viewpoint constraint （SVC） is the principal optical characteristic for most catadioptric omnidirectional vision （COV）. Precise calibration for SVC is needed during system assembling. Owning to the nonlinear distortion in the imaging system,the precision of calibration based on ideal perspective imaging model is often poor. A new calibration method of SVP for the COV was proposed. Firstly,an image correction algorithm was obtained by training a neural network. Then,according to characteristics of space circular perspective projection,the corrected image of the mirror boundary was used to estimate whose position and attitude relative to the camera to guidance calibration. Since the estimation was conducted based on actual image model rather than the simplified model,the estimation error is largely reduced,and the calibration accuracy is clearly improved. Experiments are conducted on simulated images and real images to evaluate the accuracy and the effectively of the method.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2010年第7期115-118,共4页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(60875025)
关键词
折反射全方位视觉
单视点约束
透视投影
神经网络
非线性畸变
光学畸变校正
catadioptric omnidirectional vision single viewpoint constraint perspective projection neural network nonlinear distortion optical distortion calibration
