基于双目视觉及DSP的农田障碍物检测与路径识别方法研究
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摘要
农田移动机器人属于机器人研究领域内的热点之一。在农田移动机器人相关研究中，农田障碍物检测与路径识别是机器人能够正常工作的关键，也是实现其真正智能化和完全自主移动的核心技术。利用双目视觉技术实现农田障碍物检测，具有探测范围广、检测精度高等特点，利用DSP技术实现路径的识别，具有实时性好、处理速度快、体积小、功耗低等优势，因此开展本论文的研究具有重要的理论意义和实用价值。
本文围绕农业机器人的自动导航，提出了一种基于双目视觉及DSP技术的农田障碍物检测与路径识别方法，研究出一种农田障碍物的快速检测方法，并实现了基于DSP的路径识别软、硬件系统的搭建，主要包括以下工作：
（1）建立了双目视觉的成像模型，采集多组不同位姿的标定物图像进行摄像机标定，利用计算方法和几何变换得出摄像机的内、外部参数，经过多次实验，得出标定的像素误差基本在0.2个像素范围内，满足系统的要求，可用于后续的计算。
（2）介绍了基于双目视觉技术的多种农田障碍物检测方法，从图像获取、双目校正、立体匹配和深度测量四个方面进行讨论，首先利用标定结果对田间图像对进行去畸变，然后利用Bouguet算法进行校正，接着采用基于绝对差值和的局部匹配算法获得了较理想的视差图，最后通过三角测量计算障碍物的深度信息。编写程序代码，并在实验室与农田环境中进行大量实验，可实现多种障碍物的检测，如人、砖块、铁锹、陷坑等，实验证明，当检测范围在2000mm以内时，检测结果误差较小，具有较好的稳定性和鲁棒性。
（3）介绍了基于DSP的路径识别方法。搭建了以北京合众达公司的SEED-VPM642为核心的硬件实验平台，该硬件平台采用专用于视频处理的高性能DSP作为处理核心，研究过程中采用了模块化的思想，其中包括DSP系统（CSL库函数、EMIFA接口、EDMA、视频编解码芯片等）初始化模块、视频图像的输入与输出模块、DSP核心图像处理算法模块等，完成了整个DSP系统的仿真与测试，实验表明，该方法处理速度快、实时性较好，满足系统的要求。
Agricultural mobile robot research is one of the hotpots in the robot research field.Inrelated research of agricultural mobile robot,obstacle detection and path identification are notonly the key of realizing robot to work, but also the core technology to achieve the realintelligent and autonomous move. Obstacle detection based on binocular stereo vision has theadvantage of wide range of detection and high accuracy, path identification based on DSPtechnology has the advantage of good real-time, fast processing speed，small size, low powerconsumption, and so on, so the research of this paper has important theoretical significanceand practical value.
This paper focuses on the autonomous navigation of agricultural robots, developed arapid obstacle detection method in farmland based on binocular vision and path identificationmethod based on DSP technology, and setted up the software and hardware system, includingthe following work:
(1) Established the imaging model of binocular vision, finished camera calibration withgroups of object images in different position, got the camera internal and external parametersby calculation methods and geometric transformations, after many experiments, the pixelerror of calibration was within0.2pixels, it can meet the requirement of the system and canbe used in subsequent calculations.
(2) Introduced a variety of obstacles detection method in farmland based on binocularvision. technology, focus on the real-time inages acquisition, binocular camera calibration,binocular correction, stereo matching and depth measurement and so on, firstly the imagesreseived should be distorted by the result of camera calibration, and then used Bouguetalgorithm rectified the image pairs, then striked the disparity map using the local matchingalgorithm based on absolute difference, finally calculated the depth information of theobstacles by triangulation method. Programmed the code, and took a large number ofexperiments in the laboratory and field environment, we can achieve the detection of a varietyof obstacles, such as people, bricks, shovels, crater, etc. Experiments showed that when the detection range was less than2000mm, the errors were less,it showed that the method hasgood stability and robustness.
(3) Introduced a path identification method based on DSP technology. Setted up thehardware experiment platform, it's core is the SEED-VPM642which is developed by BeijingHezhongda company, the hardware platform used a dedicated high-performance DSP as theprocessing core, using the idea of modular while the research process, including DSP system（CSL library functions, EMIFA interface, the EDMA, video codec chip, etc.）initialization,the input and output of video, the DSP core image processing algorithms, etc., completed thesimulation and testing of the entire DSP system. Experiments showed that the method had fastprocessing speed and better real-time, can meet the requirement of the system.
本文围绕农业机器人的自动导航，提出了一种基于双目视觉及DSP技术的农田障碍物检测与路径识别方法，研究出一种农田障碍物的快速检测方法，并实现了基于DSP的路径识别软、硬件系统的搭建，主要包括以下工作：
（1）建立了双目视觉的成像模型，采集多组不同位姿的标定物图像进行摄像机标定，利用计算方法和几何变换得出摄像机的内、外部参数，经过多次实验，得出标定的像素误差基本在0.2个像素范围内，满足系统的要求，可用于后续的计算。
（2）介绍了基于双目视觉技术的多种农田障碍物检测方法，从图像获取、双目校正、立体匹配和深度测量四个方面进行讨论，首先利用标定结果对田间图像对进行去畸变，然后利用Bouguet算法进行校正，接着采用基于绝对差值和的局部匹配算法获得了较理想的视差图，最后通过三角测量计算障碍物的深度信息。编写程序代码，并在实验室与农田环境中进行大量实验，可实现多种障碍物的检测，如人、砖块、铁锹、陷坑等，实验证明，当检测范围在2000mm以内时，检测结果误差较小，具有较好的稳定性和鲁棒性。
（3）介绍了基于DSP的路径识别方法。搭建了以北京合众达公司的SEED-VPM642为核心的硬件实验平台，该硬件平台采用专用于视频处理的高性能DSP作为处理核心，研究过程中采用了模块化的思想，其中包括DSP系统（CSL库函数、EMIFA接口、EDMA、视频编解码芯片等）初始化模块、视频图像的输入与输出模块、DSP核心图像处理算法模块等，完成了整个DSP系统的仿真与测试，实验表明，该方法处理速度快、实时性较好，满足系统的要求。
Agricultural mobile robot research is one of the hotpots in the robot research field.Inrelated research of agricultural mobile robot,obstacle detection and path identification are notonly the key of realizing robot to work, but also the core technology to achieve the realintelligent and autonomous move. Obstacle detection based on binocular stereo vision has theadvantage of wide range of detection and high accuracy, path identification based on DSPtechnology has the advantage of good real-time, fast processing speed，small size, low powerconsumption, and so on, so the research of this paper has important theoretical significanceand practical value.
This paper focuses on the autonomous navigation of agricultural robots, developed arapid obstacle detection method in farmland based on binocular vision and path identificationmethod based on DSP technology, and setted up the software and hardware system, includingthe following work:
(1) Established the imaging model of binocular vision, finished camera calibration withgroups of object images in different position, got the camera internal and external parametersby calculation methods and geometric transformations, after many experiments, the pixelerror of calibration was within0.2pixels, it can meet the requirement of the system and canbe used in subsequent calculations.
(2) Introduced a variety of obstacles detection method in farmland based on binocularvision. technology, focus on the real-time inages acquisition, binocular camera calibration,binocular correction, stereo matching and depth measurement and so on, firstly the imagesreseived should be distorted by the result of camera calibration, and then used Bouguetalgorithm rectified the image pairs, then striked the disparity map using the local matchingalgorithm based on absolute difference, finally calculated the depth information of theobstacles by triangulation method. Programmed the code, and took a large number ofexperiments in the laboratory and field environment, we can achieve the detection of a varietyof obstacles, such as people, bricks, shovels, crater, etc. Experiments showed that when the detection range was less than2000mm, the errors were less,it showed that the method hasgood stability and robustness.
(3) Introduced a path identification method based on DSP technology. Setted up thehardware experiment platform, it's core is the SEED-VPM642which is developed by BeijingHezhongda company, the hardware platform used a dedicated high-performance DSP as theprocessing core, using the idea of modular while the research process, including DSP system（CSL library functions, EMIFA interface, the EDMA, video codec chip, etc.）initialization,the input and output of video, the DSP core image processing algorithms, etc., completed thesimulation and testing of the entire DSP system. Experiments showed that the method had fastprocessing speed and better real-time, can meet the requirement of the system.
引文
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