Factor graph-based high-precision visual positioning for agricultural robots with fiducial markers

Abstract

High-precision positioning of agricultural robots is the key to the automation of greenhouse agricultural production. We propose a visual positioning method by leveraging the fiducial markers and factor graph to achieve reliable performance. This method consists of two parts, a front-end module and a back-end module. First, fiducial markers are involved to the front-end module to tackle the problems of unstructured and insufficient features in the greenhouse environment. A tag detection algorithm AprilTag2 is used to identify markers and extract feature points for the positioning purpose. Second, to eliminate the adverse effects of camera motion, mechanical vibration, and environmental disturbance on the positioning accuracy, the back-end module considers the characteristics of robot motion and the constraint relationship of variables, and correspondingly designs a positioning model based on the factor graph. The model combines five factors for maximum a posteriori estimation and incremental optimization of the state of the robot in real time. Finally, the positioning experiment of the wheeled robot was carried out at the motion speed of 0.26 m/s, 0.39 m/s, and 0.52 m/s. The experimental results show that the average errors of positioning are 0.056 m, 0.065 m, and 0.081 m, respectively, and the standard deviations of the errors are lower than 0.05 m. The visual positioning method based on fiducial markers and factor graph outperforms the mainstream positioning techniques in terms of positioning accuracy and robustness, and meet the high-precision positioning requirements in complex agricultural fields.