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机器人视觉技术及实现方法研究
其他题名Research of Robot Vision Technology and its Implementation
周庆瑞
学位类型工学博士
导师原魁
2005-10-26
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
关键词机器人视觉 嵌入式视觉系统 Fpga 图像分割 并行算法 Robot Vision Embedded Vision System Fpga Image Segmentation Parallel Algorithms
摘要视觉系统对于智能机器人感知外部环境是至关重要的,它直接影响机器人的性能。虽然对机器人视觉的研究已经取得了很大进展,但是离人们的期望还相去甚远,仍有很多问题有待解决。视觉处理的实时性是机器人视觉走向实用化需解决的主要问题之一。本文在国家863计划和中科院自动化研究所创新基金的资助下,从高性能视觉平台的研制和实时并行图像处理算法的研究入手,对机器人视觉进行深入的研究,主要贡献如下: 1.针对移动机器人对视觉系统的需求,研制开发了一种基于FPGA和DSP,具有多种通信接口、并行运算能力和模块化特点的,通用、高性能、低功耗嵌入式视觉平台;并在设计中实现了多处技术创新。 2.提出了一种拟全方位视觉系统和多尺度注视视觉方法。该视觉系统具有高速并行工作能力,能够满足机器人对视野的全方位和图像的高精度要求,并且为高复杂度算法的运行提供了可能。使用多尺度注视视觉方法进一步提高了系统的性能。 3.提出了一种在YUV空间,每个通道只用6位数据建立三维颜色查找表的彩色图像分割算法,并利用统计的方法进行颜色学习,从而提高分类的准确性和鲁棒性。最后在FPGA上实现了该算法,极大地提高了算法的实时性。 4.提出了一种新的连通区域标注算法,用一种特殊的存储结构表示算法中使用的等价标号表,并在FPGA中实现了该算法,和传统的方法相比该算法具有很高的实时性。
其他摘要Vision system is very important for intelligent robots to sense surrounding environment, which influences the performance of robots directly. Although great progresses have been achieved, there are lots of problems needing to be resolved on robot vision. The real-time performance of algorithm is the main bottleneck against robot vision working in practice. Supported by 863 National High-tech Project and Innovation Foundation of IACAS, we have developed an embedded vision system of high performance and studied some algorithms of real-time image processing. The novel work and contribution of this thesis can be summarized as follows: 1.Based on the technologies of DSP and FPGA, an embedded vision system of high performance is developed, which has architecture of modularization and can provide various communication interface. 2.A pseudo omnidirectional vision system and a multi-scale focus strategy are presented. This vision system can provide omnidirectional view of surroundings with high quality. Using the multi-scale focus strategy further improves the performance of vision system. 3.A real-time color image classification method is presented. In order to calibrate the object’s color in different lighting conditions, a kind of statistic ellipsoidal model is constructed. In this method, a 3-D Color Look-up Table (CLUT) is built in which only 18 bits are needed to represent one kind of color, instead of conventional 24 bits. Moreover, this method is implemented on FPGA, which can highly reduce the CPU’s computation burden and remarkably improve the performance of vision system. 4.Based-on FPGA, a fast algorithm for labeling connected component in binary image is presented, in which a novel method of dealing with equivalent labels is used and implemented in FPGA by a ACAM(Address and Content- Addressable Memories) memory. Contrast to traditional method, this algorithm has much higher real-time performance.
馆藏号XWLW955
其他标识符200218014603188
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5879
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
周庆瑞. 机器人视觉技术及实现方法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2005.
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