基于图像处理技术的移动机器人目标跟踪

【作者】 沈哲；

【导师】 刘祚时；

【作者基本信息】 江西理工大学 ， 机械电子工程， 2010， 硕士

【摘要】 移动机器人作为21世纪最伟大的发明现在越来越多的应用于工业生产和日常生活当中。其中对机器人的视觉研究更是移动机器人领域的核心问题之一。机器视觉的一个主要研究方向就是运动目标的识别与跟踪。在大多数情况下,人们一般只对目标和运动物体感兴趣。比如生产线上对产品的检测,汽车的导航与自动驾驶,无人飞机等等。因此,对运动目标的跟踪的研究有着很大的现实意义和实际价值。本文以运动的物体为目标,在室内环境下对运动目标进行识别与跟踪。文章主要从图像处理和目标跟踪两方面对移动机器人的目标跟踪进行研究。在图像处理方面,分析现有方法的优缺点,在此基础上提出了一种结合目标形状特征和颜色特征的分割方法。在阈值分割后继续做边缘提取,通过目标的几何特点,计算物体的形状参数,从而识别目标。实验证明此方法可以在复杂的环境下提取我们感兴趣的目标。在目标跟踪过程中,重点研究了Mean-Shift算法并将其用到目标跟踪当中,分析其优缺点并提出将无迹卡尔曼滤波引入Mean-Shift算法当中的方法。通过无迹卡尔曼滤波的预测机制去估计目标下一帧中的位置,在此基础上使用Mean-Shift算法搜索估计区域,得到目标的真实位置,可以大大提高对快速目标跟踪的准确性。实验证明此方法在目标跟踪领域取得了比较理想的效果。本文对各种设计方法进行了编程实现,所有程序是在XP系统下采用VC++6.0完成的,使用了广茂达ASR机器人自带的函数库和OPENCV函数库,大大简化了编程过程。最后实验表明,上述几种算法运算速度较快,满足系统实时性要求,跟踪目标的准确度较高,满足准确性要求且易于硬件实现,达到预期效果。更多还原

【Abstract】 Mobile robot, as the greatest invention of the 21st century, is more and more used in industrial production and daily life. Research of robot vision is the core issues of mobile robots. The identification and tracking of moving targets is the main research direction of machine vision. In most cases, people often interested in the target and moving objects. Such as, product testing in the production line, car navigation and automatic driving, unmanned aircraft and so on. Therefore, the study of track for moving target have great practical significance and practical value.In this paper, the target is movement objects, to identify and tracking moving targets in the indoor. Studying the target tracking of mobile robot mainly from the image processing and target tracking.In image processing, in this based on analyzing strengths and weaknesses of existing methods, it made the segmentation method that combined the features of target shape and color. Continue to do edge detection after the threshold segmentation, through the geometric characteristics of target, calculate shape parameters of the object, and thus identify the target. Experiments proved that this method can extract goal in a complex environment that we are interested in. In the process of target tracking, the focus on the mean-shift algorithm it is used to track target,analyze the algorithm’s strengths and weaknesses and propose the method that introduce the unscented Kalman filter algorithm to the mean-shift. Through the unscented Kalman filter’s prediction mechanism to estimate the target location of the next frame, on this basis, the real target location is kown trough using of mean-shift algorithm to estimate search area, it can greatly enhance the accuracy of the fast-target tracking. Experiments proved that this method can achieve ideal results in the field of target tracking.In this paper, various design methods were programmed, all programs is achieved in the XP system and used VC + +6.0 , using function library and OPENCV libraries of the Hiroshige ASR robot’s, this greatly simplified the programming process. The last experiments show that the above-mentioned had faster computation speed, met the real-time requirements, had high degree of accuracy to track targets, filled the accuracy requirements and had easy hardware implementation, to achieve the desired results.更多还原