The calibration of cameras is a crucial step in machine vision and usually relies on an accurate detection and localization of calibration patterns in images. Therefore, checkerboards are often used, allowing precise subpixel estimation of their corners. However, noise in localization generates a proportional noise in the derived model parameters. Therefore, it is important that the localization has a certain robustness against image noise. This is even more important for deteriorated imaging conditions strongly affecting subpixel detectors. This paper presents a new checkerboard corner detector based on a localized Radon transform implemented by large box filters making it robust to low contrast, image noise, and blur while maintaining high subpixel accuracy.