Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 174, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2020.105508
Keywords
Picking robot; Multi-vision; 3D positioning; Stereo matching; Point cloud stitching
Funding
- Science and Technology Planning Project of Guangdong Province [2019A050510035]
- Key-area Research and Development Program of Guangdong Province [2019B020223003]
- Key Generic Technology System of the Agricultural Industry in Guangdong Province [2019KJ139]
- Natural Science Foundation of Guangdong Province [2015A030310465]
- General program of Chongqing Natural Science Foundation [cstc2019jcyj-msxmX0812]
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Automatic vision-based picking in orchards and fields is a highly challenging task. The orchard banana central stock, which is large in size, low in color contrast, and falls within a complex background, was taken as the subject in this research. A measurement framework based on multi-vision technology was established, and a set of general methods were utilized to improve the comprehensive performance of multi-view-geometry-based vision modules in orchard picking tasks. Multiple cameras at different angles were deployed to maximize the perception range. The global geometric parameters of the cameras were calibrated and a robust semantic segmentation network was trained to achieve effective image pre-processing. A novel adaptive stereo matching strategy was designed to ensure that the robot reliably completes 3D triangulation at various depths as it moves across the target area. Global calibration errors were corrected via a high-accuracy point cloud stitching algorithm. Experimental results indicated that the proposed adaptive stereo matching strategy was accurate to different sampling depths and showed stable performance, and the proposed point cloud stitching algorithm accurately stitched multi-view point clouds. This work provides theoretical and practical references for the 3D sensing of banana central stocks in complex environments. The proposed technique was designed for adaptability of the multi-vision system for field perception, so it can be easily transferred to similar applications such as the 3D reconstruction of agricultural targets, 3D positioning of fruit clusters, and 3D robotic arm obstacle avoidance.
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