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基于ARM的嵌入式视觉定位系统
其他题名An ARM Processor Based Embedded Vision Positioning System
邹伟
学位类型工学硕士
导师徐德 ; 喻俊志
2010-05-18
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业控制理论与控制工程
关键词嵌入式视觉 Arm处理器 自适应阈值 图像处理 特征提取 Ransac Pnp定位 视觉定位 Embedded Vision Arm Processor Adaptive Threshold Image Processing Feature Extraction Ransac Pnp-based Positioning Visual Positioning
摘要本文是在国家863计划项目“竞技与娱乐多机器人系统”的支持下完成的。本文针对仿人机器人的局部视觉进行研究,实现仿人机器人之间的相对视觉定位。 本文设计了一种基于ARM的嵌入式视觉定位系统,实现了对多目标的图像采集、图像处理和视觉定位,定位结果利用串口输出。该系统的ARM处理器采用三星公司生产的S3C2440,摄像头采用C3188A型CMOS摄像头,程序采用C语言编写。针对仿人机器人上的色标目标,进行了图像采集、处理和测量定位实验,测量速率约10次/秒。尺寸为140210mm的色标,在距离为1000~2000mm范围内的定位误差小于30mm。 提出了一种基于图像平均灰度的自适应阈值设定方法,提高了视觉系统对光照变化的自适应性。为降低自适应阈值设定的计算复杂度,采用网格法进行图像灰度值扫描,得到图像的平均灰度。在不同的光照条件下,分别计算图像的平均灰度和目标的颜色值,建立了图像平均灰度和目标分割阈值之间的函数关系。根据当前图像的平均灰度,利用该函数关系,得到当前的目标分割阈值。 提出了一种精度高、速度快的色标特征提取算法。以色标块的中心区域为起点,以水平和竖直方向按照一定间隔快速搜索色标块的4组边缘点。针对每组边缘点,利用RANSAC方法求取边缘的直线方程。利用4条边缘直线求交点,得到色标块的精确角点。然后,利用PnP方法确定出色标块在摄像机坐标系下的位置和姿态,即实现视觉定位。 最后,针对所开发的嵌入式视觉定位系统,进行了色标块识别分割、特征提取、目标定位、移动机器人目标趋近及避障等方面的实验。实验结果表明该视觉定位系统具有定位精度高、鲁棒性好等特点。
其他摘要This dissertation is supported by the National High Technology Research and Development Program of China under the program “Sport and Entertainment Multi-robot System”. This dissertation is focused on the research of local vision systems to realize relative positioning among humanoid robots. An ARM-based embedded vision positioning system is designed, which can realize the image capturing, image processing and vision positioning for multiple objects. The positioning results are output via serial port. In the designed vision system, S3C2440 produced by SAMSUNG Company is selected as the ARM processor. A CMOS camera C3188A serves as the vision sensor. The software is programmed with C language. The experiments including image capturing, image processing and vision positioning are well conducted for color marked blocks attached on humanoid robots. The measuring rate is about 10 frames per second. The positioning error is less than 30 mm when the target with size of 140210 mm is 1000~2000 mm far away from the vision system. An adaptive threshold setting method based on the average image gray level is proposed. It increases the adaptivity of the vision system to light variation. In order to reduce the computational complexity of the adaptive threshold setting method, grid method is adopted to scan the image and compute the average image gray level. In the different light conditions, the average image gray level and target color value are computed. Then the relation between the average image gray level and the threshold is established with a function. The current threshold can be easily determined according to the current average image gray level and the function. A new feature extraction approach with high speed and precision is developed for color marked blocks. The center of the color marked block is selected as the start point. The 4 groups of points on the block edges are searched in horizontal and vertical directions with specified interval. The line equation is computed for each edge with RANSAC method from each group of edge points. The 4 corner points are determined with the intersections of the lines. Then PnP-based method is employed to estimate the position and orientation of the color marked block in the camera frame. For the designed embedded vision positioning system, many experiments are conducted, such as color block identification and segmentation, feature extraction, objects positioning, object approaching a...
馆藏号XWLW1537
其他标识符200728014628046
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7504
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
邹伟. 基于ARM的嵌入式视觉定位系统[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
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