果蔬采摘机器人立体视觉技术研究
摘要
本文将对果蔬采摘机器人立体视觉技术进行研究,通过建立立体视觉成像系统,对三目摄像机采集的三幅不同角度的果蔬图像进行处理和分析,实现果蔬的分辨并获得其位置信息,为机械手的自主抓取提供依据。
文中首先对立体视觉系统的组成模块和基本原理进行了介绍,建立了考虑一阶径向畸变的摄像机模型,采用分步法对摄像机进行了标定。然后对所采集的图像完成了平滑、阈值分割等预处理过程,得到了突出感兴趣区域的二值分割图像。在此基础上,基于边缘检测技术获取了目标的边缘信息,并进一步对其进行角点提取。最后针对立体匹配环节提出了基于兴趣点的三目立体匹配方法,利用图像叠合产生的三目摄像机深度计算模型,对获取的匹配点进行计算,得到了目标物体的深度信息。
Fruit and vegetable harvesting operation is a strength, long time-consuming, highly request work, and have certain hazardous. With the aging of the population and the transfer of agricultural labor, harvesting operation urgently needs high efficiency, current, low-cost robot technology to improve productivity and quality of operations, improve the production environment. Along with the development and the application of the technology of precision agriculture, as one of the effective equipments in the field of precision agriculture, the research and application of the robot with intelligent and stereo vision had been paid more attention to and been developed gradually.
The principle of stereo vision needs to acquire two or more scene images of different angles at first, and then calculate the disparity between the pixels of these images to obtain the target object’s 3D information, this process is similar to the feeling of human beings. Along with the development of stereo vision technology, the trinocular stereo vision system comes forth, through matching among the information of images acquired by three cameras simultaneously, it can solve some problems in binocular stereo vision system effectively. Eliminate the ambiguity and reduce the difficulty of matching.
On the base of representative binocular stereo vision technology, this paper introduced some component modules and basic principles of stereo vision system, and narrated some problems and difficulties of vision technology’s application research on harvesting machine. According to the introduction of the trinocular stereo vision technology, this paper established a camera model with one rank radial aberration, and used radial alignment constraint method to calibrate the camera. The process of calibration can determine the location, attribute parameters of the camera, and make it be convenient to make sure the relationship between target object and its imaging.
Aim at raw images acquired by camera, this paper strengthened the interested target by using image division technology. Then based on classic edge detect technology to acquire edge information of target object, and obtained corners of it by using Harris’s method. Corners retained rich characteristic information of target object, they could provide adequate restraint to analysis and processing of the target. Meanwhile, this process reduced the amount of information of image, and also the amount of operation of matching process, and improved the speed of entire system.
The most difficult and complex step of stereo vision is namely stereo match. In order to solve this step, this paper advanced trinocular stereo matching method based on corners, and used the depth model of trinocular camera produced by superimpose images to match these corners, which aimed to obtain depth information of the fruit and vegetable. Trincular stereo match method can overcome some natural limitations of binocular stereo vision technology effectively, such as its accuracy depends on the depth of target object and the length of baseline of two cameras. Meanwhile, this paper used corners as matching characteristics in ternary matching, in this situation, matching errors will happen only in occasional circumstance. In this way, this method can ensure the reliability of match result.
Finally, this paper made use of stereo vision technology to construct experimental platform including trinocular camera, image acquisition card, image processing computer, and used this system to do a range of processing to three images including the same target, for example, adjustment and edge detect, acquire the depth image of target fruit and vegetable. It can direct autonomy grab of the fruit and vegetable harvesting robot, and achieve the purpose of this paper.
文中首先对立体视觉系统的组成模块和基本原理进行了介绍,建立了考虑一阶径向畸变的摄像机模型,采用分步法对摄像机进行了标定。然后对所采集的图像完成了平滑、阈值分割等预处理过程,得到了突出感兴趣区域的二值分割图像。在此基础上,基于边缘检测技术获取了目标的边缘信息,并进一步对其进行角点提取。最后针对立体匹配环节提出了基于兴趣点的三目立体匹配方法,利用图像叠合产生的三目摄像机深度计算模型,对获取的匹配点进行计算,得到了目标物体的深度信息。
Fruit and vegetable harvesting operation is a strength, long time-consuming, highly request work, and have certain hazardous. With the aging of the population and the transfer of agricultural labor, harvesting operation urgently needs high efficiency, current, low-cost robot technology to improve productivity and quality of operations, improve the production environment. Along with the development and the application of the technology of precision agriculture, as one of the effective equipments in the field of precision agriculture, the research and application of the robot with intelligent and stereo vision had been paid more attention to and been developed gradually.
The principle of stereo vision needs to acquire two or more scene images of different angles at first, and then calculate the disparity between the pixels of these images to obtain the target object’s 3D information, this process is similar to the feeling of human beings. Along with the development of stereo vision technology, the trinocular stereo vision system comes forth, through matching among the information of images acquired by three cameras simultaneously, it can solve some problems in binocular stereo vision system effectively. Eliminate the ambiguity and reduce the difficulty of matching.
On the base of representative binocular stereo vision technology, this paper introduced some component modules and basic principles of stereo vision system, and narrated some problems and difficulties of vision technology’s application research on harvesting machine. According to the introduction of the trinocular stereo vision technology, this paper established a camera model with one rank radial aberration, and used radial alignment constraint method to calibrate the camera. The process of calibration can determine the location, attribute parameters of the camera, and make it be convenient to make sure the relationship between target object and its imaging.
Aim at raw images acquired by camera, this paper strengthened the interested target by using image division technology. Then based on classic edge detect technology to acquire edge information of target object, and obtained corners of it by using Harris’s method. Corners retained rich characteristic information of target object, they could provide adequate restraint to analysis and processing of the target. Meanwhile, this process reduced the amount of information of image, and also the amount of operation of matching process, and improved the speed of entire system.
The most difficult and complex step of stereo vision is namely stereo match. In order to solve this step, this paper advanced trinocular stereo matching method based on corners, and used the depth model of trinocular camera produced by superimpose images to match these corners, which aimed to obtain depth information of the fruit and vegetable. Trincular stereo match method can overcome some natural limitations of binocular stereo vision technology effectively, such as its accuracy depends on the depth of target object and the length of baseline of two cameras. Meanwhile, this paper used corners as matching characteristics in ternary matching, in this situation, matching errors will happen only in occasional circumstance. In this way, this method can ensure the reliability of match result.
Finally, this paper made use of stereo vision technology to construct experimental platform including trinocular camera, image acquisition card, image processing computer, and used this system to do a range of processing to three images including the same target, for example, adjustment and edge detect, acquire the depth image of target fruit and vegetable. It can direct autonomy grab of the fruit and vegetable harvesting robot, and achieve the purpose of this paper.
引文
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[7]方建军．移动式采摘机器人研究现状与进展[J]．农业工程学报，2004，20(2)：273-278．
[8]应义斌，章文英，蒋亦元等．机器视觉技术在农产品收获和加工自动化中的应用[J]．农业机械学报，2005，31(3)：112-115．
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[12]赵匀，武传宇，胡旭东等．农业机器人的研究进展及存在的问题[J]．农业工程学报，2003，19(1)：20-24．
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[18] Iida M,Furube K,Namikawa K,et al.Development of watermelon harvesting gripper [J].J Japan Soc Agric Mach,1996,58(3):19-26.
[19] Shigehiko Hayashi,Katsunobu Ganno.Yukitsugu Ishii,et al.Robotic harvesting system for eggplants[J].JARQ,2002,36(3):163-168.
[20] Murakami N.Inoue K,Otsuka K.Selective harvesting robot of cabbage[J]. JSAM, 1995,2:24-31. [21]汤建华，陈树人，尹建军．计算机视觉在农业自动化收获中的应用与展望[J]．农机化研究，2004，5，16-18．
[22]张瑞合，姬长英，沈明霞等．计算机视觉技术在番茄收获中的应用[J]．农业机械学报，200l，32(5)：50-52．
[23]徐惠荣，叶尊忠，应义斌．基于彩色信息的树上柑橘识别研究[J]．农业工程学报，2005，21(5)：98-101．
[24]张铁中，林宝龙，高锐．水果采摘机器人视觉系统的目标提取[J]．中国农业大学学报，2004：，9(2)：68-72．
[25]赵金英，张铁中，杨丽．西红柿采摘机器人视觉系统的目标提取[J]．农业机械学报，2006，37(10)：200-203．
[26]宋健，张铁中，徐丽明等．果蔬采摘机器人研究进展与展望[J].农业机械学报，2006，37(5)：158-162．
[27]章毓晋．图像工程下册：图像理解与计算机视觉[M]．北京：清华大学出版社，1999．
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