Height-Variable Monocular Vision Ranging Technology for Smart Agriculture

Smart agriculture utilizes a variety of advanced technologies to promote sustainable agriculture and provide solutions for intelligent, automated and unmanned agriculture. Agricultural robots and related technologies are an important part of smart agriculture, while autonomous navigation is a core function of autonomous agricultural robots, which rely on information about the distance of obstacles in a scene to support decision making. In this paper, we propose a ground point geometric ranging model, which can be used in camera height dynamic change scenarios, and the method is validated by model derivation and hypothesis testing. The model combines ranging and camera calibration, choosing to compensate for distortion and defocus phenomena caused by nonlinear imaging of the camera to the focal length, and completes the parameter calibration using a small amount of ground point real distance data. In this paper, the YOLOv8 model is used to identify and range outdoor cattle, and the experimental results show that the lowest range accuracy of this method reaches 95%, this method eliminates the dependence on camera height for focal calibration in ranging models, and in practice requires only once focal calibration for permanent use, achieving a significant reduction in the complexity of focal calibration, and the migrability of the model in scenarios where the camera height changes.