This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclinat...This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclination of the optical axis is thoroughly considered with respect to the image plane,and a rigorous imaging model including 8 unknown intrinsic parameters is built.Second,the basic calibration equation based on star vector observations is presented.Third,the partial derivative expressions of all 11 camera parameters for linearizing the calibration equation are deduced in detail,and an iterative solution using the least squares method is given.Furtherly,simulation experiment is designed,results of which shows the new model has a better performance than the old model.At last,three experiments were conducted at night in central China and 671 valid star images were collected.The results indicate that the new method obtains a mean magnitude of reprojection error of 0.251 pixels at a 120°FoV,which improves the calibration accuracy by 38.6%compared with the old calibration model(not considering the inclination of the optical axis).When the FoV drops below 20°,the mean magnitude of the reprojection error decreases to 0.15 pixels for both the new model and the old model.Since stars instead of manual control points are used,the new method can realize self-calibration,which might be significant for the long-duration navigation of vehicles in some unfamiliar or extreme environments,such as those of Mars or Earth’s moon.展开更多
摄像机标定是由二维图像获取三维空间信息过程中的重要步骤,是计算机视觉领域的研究热点。文中通过改进摄像机数学模型,提出一种快速精确的摄像机标定方法。该方法以平面标定法为基础,同时引入径向畸变和切向畸变以提高标定精度。在此...摄像机标定是由二维图像获取三维空间信息过程中的重要步骤,是计算机视觉领域的研究热点。文中通过改进摄像机数学模型,提出一种快速精确的摄像机标定方法。该方法以平面标定法为基础,同时引入径向畸变和切向畸变以提高标定精度。在此基础上结合Open CV库,在Visual Studio 2015开发环境下实现了摄像机标定,并进行了实验验证,实验结果表明该方法具有较高的标定精度,计算速度快,过程稳定。展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.42074013 and 41704006)。
文摘This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclination of the optical axis is thoroughly considered with respect to the image plane,and a rigorous imaging model including 8 unknown intrinsic parameters is built.Second,the basic calibration equation based on star vector observations is presented.Third,the partial derivative expressions of all 11 camera parameters for linearizing the calibration equation are deduced in detail,and an iterative solution using the least squares method is given.Furtherly,simulation experiment is designed,results of which shows the new model has a better performance than the old model.At last,three experiments were conducted at night in central China and 671 valid star images were collected.The results indicate that the new method obtains a mean magnitude of reprojection error of 0.251 pixels at a 120°FoV,which improves the calibration accuracy by 38.6%compared with the old calibration model(not considering the inclination of the optical axis).When the FoV drops below 20°,the mean magnitude of the reprojection error decreases to 0.15 pixels for both the new model and the old model.Since stars instead of manual control points are used,the new method can realize self-calibration,which might be significant for the long-duration navigation of vehicles in some unfamiliar or extreme environments,such as those of Mars or Earth’s moon.
文摘摄像机标定是由二维图像获取三维空间信息过程中的重要步骤,是计算机视觉领域的研究热点。文中通过改进摄像机数学模型,提出一种快速精确的摄像机标定方法。该方法以平面标定法为基础,同时引入径向畸变和切向畸变以提高标定精度。在此基础上结合Open CV库,在Visual Studio 2015开发环境下实现了摄像机标定,并进行了实验验证,实验结果表明该方法具有较高的标定精度,计算速度快,过程稳定。
