Vision measurement plays a significant role in robot automatic assembly. In this study, a new measurement method for robot peg-in-hole pre-alignment via a combined two-level vision system is proposed. The assembly system and a global coordinate system calibration procedure based on a dynamic coordinate system are developed to construct the accurate transformation between the coordinate systems. The subsequent hole pose is obtained with the proposed image processing method and hole edge matching method, followed by the 3-D hole reconstruction and spatial circle fitting algorithm. A low-cost pose determination is proposed based on hole edge point selection. The experiment and analysis indicate that the hybrid measurement system has high precision in the local hole pose and global robot pose measurement accuracy. The peg-in-hole assembly results verify the measurement and calibration accuracy sideways. Our hybrid measurement system enhances the vision measurement range and local accuracy, and robot automation.

Combining a global tracking system with a local measurement system constitutes an efficient approach for meter-scale component measurement. The transformation matrix between the local system and the transfer target is critical in the global integration of local data. This article proposes an enhanced calibration methodology for the combined track-based vision measurement system. A calibration equation based on the system data transformation and scale factor is established. Then theoretical and optimal solutions for the transformation matrix were derived. Subsequently, a statistical analysis is conducted to assess the error distribution and the impact of error sources on global measurement accuracy. Notably, the influence of the scale factor on the global error presents a linear pattern. Both simulation and experimental validations demonstrate that our calibration approach achieves high precision in determining the transformation matrix and global positioning. Specifically, the repeatability of positioning and the accuracy of data stitching between multiple viewpoints are both lower than 0.1 mm. The flatness of the point cloud stitched from two perspectives using a planar calibration board is 0.025 mm. Consequently, the proposed calibration strategy enables the accurate 3D reconstruction of meter-scale components while preserving local accuracy.

The combination of a large-field-of-view visual positioning system and a local scanning sensor affords an effective approach to the three-dimensional measurement of meter-scale objects and has been actively investigated by researchers. The calibration of such an integrated measurement system is essential to establishing the overall measurement accuracy. This paper proposes calculating the transformation relationship between the local system and the transfer target through the determination of the scales of the combined system. The principle and implementation of this tracking-based combined measurement system are described in this paper, as well as the use of the respective scales of the combined system to establish a model of the applicable geometrical relationships. The Kronecker product was used to solve the model equations, after which homogenous transformation matrix optimization was performed. An error model of the camera-based measurement system was also established based on a combination of the image distortion and the working volume. The two- and three-dimensional errors of the local and global sensors were further quantitatively simulated and visualized to demonstrate the calibration accuracy. Standard plane and complex large object measurement experiments were performed to verify the accuracy of the proposed measurement system and its use for the reconstruction of a large shape.

This paper describes a pose measurement and tracking strategy for an automatic tracking-based scanning system through developing a ball-shaped target. The target structure is introduced. Then, to determine the target pose, a coded marker and accurate center extraction method is proposed using subpixel and cross-ratio invariance principle. Furthermore, the target coordinate system is established by vision-based target calibration. A confidence-based tracker is proposed, enabling higher tracking confidence and accuracy, and keeping a high tracking speed. The stereo vision estimates the 3D position and orientation of the moving target in real-time. The trajectory is smoothed dynamically based on the Kalman filter. Experiments are implemented to verify the tracking speed, accuracy, and spatial accessibility in robot tracking applications. Additionally, the proposed target and strategy have good performance in full-space and fall-orientation tracking.