Cross-Layer Feature Fusion and Decentration Aberration Correction of Circular Points for Automated Guided Vehicle Terminal Positioning

Abstract

To address the visual detection and positioning challenge of the Automated Guided Vehicle (AGV) terminal, this study proposed a high-precision target recognition and positioning strategy based on cross-layer feature fusion and eccentricity error correction. In the remote coarse positioning stage, small number of receiver domains underwent continuous multi-layer convolution, which broadened the receptive field of a single element in the feature map of a small circular target. Next, a cross-layer connection feature pyramid was constructed, and the deconvolution module was utilized to enlarge the feature map and fuse it with the shallow features, focusing more on the fine-grained image recognition and enhancing the applicability of circular marker detection. In the near-end fine positioning stage, the factors impacting the deviation between the projection point of the circular feature center and the fitting center were analyzed. Based on this analysis, the deviation of the iterative fitting center was corrected to approximate the real center projection sub-pixel coordinates. The experimental results demonstrated that the proposed method achieved dynamic positioning accuracy of better than 2.0 mm, static positioning accuracy of better than 1.5 mm, and a false recognition rate of less than 1.33% in the range of 3 m from AGV to the tray. Consequently, this method has significant application potential in enabling rapid and stable terminal vision positioning.

Keywords

• Automated guided vehicles (AGV)
• target detection
• feature fusion
• decentration aberration correction
• vision positioning