A Simple Method for Measuring Monocular Visual Pose

针对特定工作条件下机器人位姿测量问题,研究和设计了一种简易的基于单目视觉的位姿测量系统,提出了一种精确的靶标检测算法与单目视觉测量方法相结合的方案,可实现机器人位姿的测量。首先,通过图像预处理和靶标检测算法提取靶标在图像中的坐标,其中图像预处理包括中值滤波、边缘检测和图像形态学膨胀等过程。然后进行测量模型的标定,用最小二乘法求解模型参数,利用建立的测量模型进行坐标系的转换,将所得靶标图像二维像素坐标转换成三维世界坐标,从而得到靶标在世界坐标系中的位姿。实验证明了该方法的准确性和有效性,满足测量要求

A Simple Method for Measuring Monocular Visual Pose

针对特定工作条件下机器人位姿测量问题,研究和设计了一种简易的基于单目视觉的位姿测量系统,提出了一种精确的靶标检测算法与单目视觉测量方法相结合的方案,可实现机器人位姿的测量。首先,通过图像预处理和靶标检测算法提取靶标在图像中的坐标,其中图像预处理包括中值滤波、边缘检测和图像形态学膨胀等过程。然后进行测量模型的标定,用最小二乘法求解模型参数,利用建立的测量模型进行坐标系的转换,将所得靶标图像二维像素坐标转换成三维世界坐标,从而得到靶标在世界坐标系中的位姿。实验证明了该方法的准确性和有效性,满足测量要求

Traffic sign recognition technology based on BOW model

According to several key technologies in automatic identification technology of traffic signs, this paper makes a detailed study. First of all, from traffic signs segmentation algorithm, a segmentation algorithm based on iterative segmentation and maximum variance between clusters of traffic signs is studied. Secondly, with feature extraction of traffic signs based on SIFT studied, the codebook is generated by these feature clustering and images are described by histograms using Bag of Words (BOW) model. Finally, multi-class classifier based on SVM is designed to classify traffic signs. The experimental results demonstrate the effectiveness and practicality of the BOW model classification algorithm based on the traffic sign images collected in the natural environment. © 2013 ACADEMY PUBLISHER

Zooming image based false matches elimination algorithms for robot navigation

Feature matching is one of the most important steps in the location technology of zooming images. According to the scale-invariant feature transform matching algorithm, several improved false matches elimination algorithms are proposed and compared in this article. First, features of zooming images and ranging models are introduced in detail in the theory framework of the scale-invariant feature transform feature detection and matching algorithm. The key role of the feature matching algorithm and false matches elimination in the ranging technology of zooming images is discussed and addressed. Second, false matches are eliminated by the proposed approach based on geometry constraint in zooming images with a higher accuracy. Third, false matches are removed by an elimination algorithm based on properties of the scale-invariant feature transform features. Finally, an iterative false matches elimination algorithm based on distance from epipole to epipolar line is proposed and this algorithm can also solve the real-time calibration of the shrink-amplify center for zooming images. Experiments results demonstrate that the three false matches elimination algorithms proposed are stable, and the false matches of feature points can be eliminated effectively with combination of these three methods, and the rest matching points can be applied into robot visual servoing.</p