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当前位置: 首页 > 文章 > 菠萝采收机械低成本双目视觉平台搭建与田间试验(英文) 农业工程学报 2012,28 (2) 188-192
Position: Home > Articles > Construction and in-field experiment of low-cost binocular vision platform for pineapple harvesting robot Transactions of the Chinese Society of Agricultural Engineering 2012,28 (2) 188-192

菠萝采收机械低成本双目视觉平台搭建与田间试验(英文)

作  者：
李斌;王海峰;黄文倩;张弛
单  位：
国家农业智能装备工程技术研究中心中国农业机械化科学研究院;中国空空导弹研究院
关键词：
机器视觉;试验;标定;菠萝采摘;双目视觉;低成本
摘  要：
视觉系统是菠萝采摘机械的关键部件之一,可为采摘终端提供待采果实的位置导航信息。考虑到菠萝果形较大,易于识别,以及系统应用于农业领域,需尽可能降低成本。该研究选取双目视觉技术,采用低成本的 CMOS 视觉传感器,辅以三脚架、双目云台,以及计算机、软件系统,搭建低成本双目视觉标定平台;研究了标定模型及流程,并基于 C++和 OpenCVv1.1 环境以及 Matlab 标定工具箱的软件环境平台,采用张正友标定算法,分别对视觉传感器进行标定试验,选取了适合本平台的标定方法。基于此平台和开发的菠萝果实识别算法,在湛江菠萝田间进行果实深度测量试验发现,果实测试距离小于1 m 时,深度误差在 6～8 cm 范围内,经软件算法校正后,误差控制在 2～3 cm 范围内,该平台试验结果良好,表明低成本试验平台具有可行性。该研究可为菠萝采摘机器人视觉系统的开发提供参考。
译  名：
Construction and in-field experiment of low-cost binocular vision platform for pineapple harvesting robot
作  者：
Li Bin 1 , Wang Haifeng 2 , Huang Wenqian 1 , Zhang Chi 1 1. National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100089; 2. Chinese Academy of Modern Agricultural Mechanization Sciences, Beijing 100083
关键词：
computer vision, experiment, calibration, pineapple harvesting, binocular vision, low-cost
摘  要：
Vision system is one of key parts of agricultural harvesting robots, which provides the fruit’s position information for navigating the manipulator. Considering its applications in agriculture and the pineapple is big enough for recognition, this study presents a low-cost binocular vision platform for pineapple harvesting robots, which consists of low-cost CMOS (Complementary Metal Oxide Semiconductor) image sensors, a tripod, a binocular pan, a PC and software system; The calibration model and the calibration software were developed based on C++ and OpenCV version 1.1, and Matlab calibration toolbox separately; The Zhang’s algorithm was employed during the calibration. By experiment, the suitable calibration method for the constructed platform was selected. Based on the low-cost vision platform and developed pineapple recognition algorithms, 3D position calculation experiments for pineapples were conducted in a pineapple field of Zhanjiang. The results showed that the depth errors were less than 6-8 cm when the depth distance was around 1 m, and the errors were less than 2-3 cm after correcting the whole system. The low-cost platform performed well and its feasibility was proved. This study can provide a reference for the development of pineapple harvesting robots.

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