GCP Deployment and Recognition System based on Light-Marker UAV

Ruikai Li, Yi Wan, Dong Wei, Yongjun Zhang, Ziqian Huang, Xiaotong Li, Changjun Zhu

Abstract. This paper addresses the heavy reliance on manual operations in control point acquisition for UAV photogrammetry and proposes an encoded control point deployment and recognition method based on a Light-Marker UAV (LMUAV). Conventional approaches rely on manual placement of control points and manual identification and measurement in images for aerial triangulation, resulting in low efficiency. To address this limitation, an LMUAV equipped with an LED array actively broadcasts its positional information as quaternary optical signals. The observing UAV performs coarse localization of the target region by integrating communication priors with the imaging model, followed by light spot segmentation and graph construction within the region of interest (ROI). Node correspondences are then recovered by constructing a template graph and an observation graph and applying Reweighted Random Walks (RRWM) graph matching. The matching robustness is further enhanced by incorporating directional point constraints and RANSAC-based geometric filtering. Based on the recovered correspondences, the encoded information is decoded through color recognition and validation, enabling automatic control point recovery. Experimental results in a cross-flight-line scenario with a single target UAV demonstrate that the proposed method achieves stable node matching and encoding–decoding, with a sequence-level accuracy of 76.32%, and a final effective decoding rate of 71.05%, while maintaining centimeter-level positioning accuracy, thereby validating its effectiveness for automatic control point acquisition in UAV mapping.