  Accurate detection and localization of X-corner on both planar and non-planar
patterns is a core step in robotics and machine vision. However, previous works
could not make a good balance between accuracy and robustness, which are both
crucial criteria to evaluate the detectors performance. To address this
problem, in this paper we present a novel detection algorithm which can
maintain high sub-pixel precision on inputs under multiple interference, such
as lens distortion, extreme poses and noise. The whole algorithm, adopting a
coarse-to-fine strategy, contains a X-corner detection network and three
post-processing techniques to distinguish the correct corner candidates, as
well as a mixed sub-pixel refinement technique and an improved region growth
strategy to recover the checkerboard pattern partially visible or occluded
automatically. Evaluations on real and synthetic images indicate that the
presented algorithm has the higher detection rate, sub-pixel accuracy and
robustness than other commonly used methods. Finally, experiments of camera
calibration and pose estimation verify it can also get smaller re-projection
error in quantitative comparisons to the state-of-the-art.
