A Novel Concentric Circular Coded Target, and Its Positioning and Identifying Method for Vision Measurement under Challenging Conditions

Abstract

Coded targets have been demarcated as control points in various vision measurement tasks such as camera calibration, 3D reconstruction, pose estimation, etc. By employing coded targets, matching corresponding image points in multi images can be automatically realized which greatly improves the efficiency and accuracy of the measurement. Although the coded targets are well applied, particularly in the industrial vision system, the design of coded targets and its detection algorithms have encountered difficulties, especially under the conditions of poor illumination and flat viewing angle. This paper presents a novel concentric circular coded target (CCCT), and its positioning and identifying algorithms. The eccentricity error has been corrected based on a practical error-compensation model. Adaptive brightness adjustment has been employed to address the problems of poor illumination such as overexposure and underexposure. The robust recognition is realized by perspective correction based on four vertices of the background area in the CCCT local image. The simulation results indicate that the eccentricity errors of the larger and smaller circles at a large viewing angle of 70° are reduced by 95% and 77% after correction by the proposed method. The result of the wing deformation experiment demonstrates that the error of the vision method based on the corrected center is reduced by up to 18.54% compared with the vision method based on only the ellipse center when the wing is loaded with a weight of 6 kg. The proposed design is highly applicable, and its detection algorithms can achieve accurate positioning and robust identification even in challenging environments.

A Robust and Effective Identification Method for Point-Distributed Coded Targets in Digital Close-Range Photogrammetry

“…In addition, when the center positioning precision was low, a GCT could not be identified, although eight ellipses were extracted. Just as was mentioned in [46], when the precision of circle extraction is low, the precision of center positioning will be bad, and it will affect the precision of subsequent measurements.…”

An Automatic and Accurate Method for Marking Ground Control Points in Unmanned Aerial Vehicle Photogrammetry

“…Coded targets can be roughly classified into dotdispersing, centripetal, and color-coded targets, among others. [48] They usually have unique appearance features and can be automatically identified by computer vision methods, and the coding information they carry can be decoded as their unique identity numbers [49], [50]. These characteristics allow them to be used as GCPs in UAV photogrammetry.…”

Improved Identification for Point-Distributed Coded Targets with Self-Adaption and High Accuracy in Photogrammetry

“…Each coded target has its distinct number of coding sequences, so they always serve as tie points or control points. For this reason, coded targets can be specially applied in fast image matching, object tracking, and automatic high-precision camera orientation [3][4][5]. According to their application areas, coded targets can be used in robot navigation, augmented reality, close-range/UAV photogrammetry, and many other fields [6,7].…”