RCDN -- Robust X-Corner Detection Algorithm based on Advanced CNN Model

TL;DR

This paper introduces RCDN, a robust CNN-based X-corner detection algorithm that achieves high accuracy and robustness under challenging conditions, improving on existing methods for applications in robotics and machine vision.

Contribution

The paper presents a novel CNN-based detection algorithm with a coarse-to-fine strategy, incorporating post-processing and refinement techniques for improved accuracy and robustness in X-corner detection.

Findings

Higher detection rate than existing methods

Achieves sub-pixel accuracy under interference

Reduces re-projection error in calibration tasks

Abstract

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.