A Distance Measurement Approach for Large Fruit Picking with Single Camera

Abstract

Target ranging is the premise for manipulators to complete agronomic operations such as picking and field management; however, complex environmental backgrounds and changing crop shapes increase the difficulty of obtaining target distance information based on binocular vision or depth cameras. In this work, a method for ranging large-sized fruit based on monocular vision was proposed to provide a low-cost and low-computation alternative solution for the fruit thinning or picking robot. The regression relationships between the changes in the number of pixels occupied by the target area and the changes in the imaging distance were calculated based on the images of square-shaped checkerboards and circular-shaped checkerboards with 100 cm2, 121 cm2, 144 cm2, 169 cm2, 196 cm2, 225 cm2, 256 cm2, 289 cm2, and 324 cm2 as the area, respectively. The 918 checkerboard images were collected by the camera within the range from 0.25 m to 1.5 m, with 0.025 m as the length of each moving step, and analyzed in MATLAB to establish the ranging models. A total of 2448 images of four oval watermelons, four pyriform pomelos, and four oblate pomelos, as the representatives of large fruit with different shapes, were used to evaluate and optimize the performance of the models. The images of the front were the input, while the imaging distances were the output. The results showed that the absolute error would be less than 0.06 m for both models and would linearly increase with a decrease in the distance. The relative error could be controlled at 5%. The results proved the proposed monocular method could be a solution for the ranging of large fruit targets.