A Theory of Single-Viewpoint Catadioptric Image Formation
International Journal of Computer Vision
Omnidirectional image-based modeling: three approaches to approximated plenoptic representations
Machine Vision and Applications - Special issue: Omnidirectional vision and its applications
Geometric Properties of Central Catadioptric Line Images and Their Application in Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Calibration Method for Misaligned Catadioptric Camera
IEICE - Transactions on Information and Systems
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Instantaneous robot self-localization and motion estimation with omnidirectional vision
Robotics and Autonomous Systems
Superresolution modeling using an omnidirectional image sensor
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The single viewpoint constraint is a principal optical characteristic for most catadioptric omnidirectional vision. Single viewpoint catadioptric omnidirectional vision is very useful because it allows the generation of geometrically correct perspective images from one omnidirectional image. Therefore precise calibration for single viewpoint constraint is needed during system assembling. However, in most image detection based calibration methods, the nonlinear optical distortion brought by lens is often neglected. Hence the calibration precision is poor. In this paper, a new calibration method of single viewpoint constraint for the catadioptric omni-directional vision is proposed. Firstly, an image correction algorithm is obtained by training a neural network. Then, according to characteristics of the space circular perspective projection, the corrected image of the mirror boundary is used to estimate its position and attitude relative to the camera to guide the calibration. Since the estimate is conducted based on actual imaging model rather than the simplified model, the estimate error is largely reduced, and the calibration accuracy is significantly improved. Experiments are conducted on simulated images and real images to show the accuracy and the effectiveness of the proposed method.