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Accurate real-time positioning of ground moving targets remains challenging in UAV-based applications, especially for consumer-grade platforms equipped only with a monocular camera. The lack of direct depth information, low-precision inertial measurement units (IMUs), and limited computational resources make reliable target positioning difficult. To address these issues, this paper proposes a real-time positioning method for moving targets using a monocular camera UAV. First, a single-strip bundle adjustment (BA) model with yaw error pre-correction is designed to improve the reliability of Position and Orientation System (POS) estimation and acceleration convergence. Second, a local surface refinement is introduced to better approximate the ground around the targets, thereby improving positioning accuracy. Extensive experiments confirm the feasibility of the proposed method. Compared with conventional BA assisted by a POS, our approach reduces the total Root Mean Square Error (RMSE) by 8.7%. and achieves 3D positioning accuracy ranging from 0.068 m to 0.123 m while maintaining real-time performance. Furthermore, compared with DSM-based ray intersection and deep learning-based depth estimation methods, the proposed framework achieves higher accuracy without requiring external terrain data or computationally expensive models, providing an efficient solution for real-time, high-precision moving target positioning using a consumer-grade monocular UAV.

Real-time positioning for ground moving targets based on monocular camera