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This work describes a novel approach to localize sub-pixel chessboard corners for camera calibration and pose estimation. An ideally continuous chessboard corner model is established, as a function of corner coordinates, rotation and shear angles, gain and offset of grayscale, and blurring strength. The ideal model is evaluated by a low-cost and high-similarity approximation for sub-pixel localization, and by performing a nonlinear fit to input image. A self-checking technique is also proposed by investigating qualities of the model fits, for ensuring the reliability of addressing perspective-n-point problem. The proposed method is verified by experiments, and results show that it can share a high performance. It is also implemented and examined in a common vision system, which demonstrates that it is suitable for on-site use.