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机器人视觉Kalman 和FIR 滤波稳像算法设计与比较

  

  • 出版日期:2013-04-30 发布日期:2015-06-11

Algorithm Design and Comparison of Kalman and FIR Filter Methods for Image Stabilization of Robot Vision

  • Online:2013-04-30 Published:2015-06-11

摘要: :稳像是提高基于视觉的移动机器人作业精度的关键。论文建立了完整的稳
像算法流程,包含图像运动学模型、KLT 特征提取、SAD 特征匹配和滤波算法;设计了运
动参数的Kalman 和FIR 滤波算法;并利用MATLAB 实现了运动参数的Kalman 和FIR 滤波
器;仿真验证和对比分析了Kalman 和FIR 滤波器对运动参数的去抖效果。结果表明,机器
人视觉稳像中,Kalman 滤波效果优于FIR 滤波。用VC++和OpenCV 编程实现了基于Kalman
滤波的机器人视觉稳像软件,在双机器人移动平台上开展了实验,稳像计算时间小于视频采
样时间,系统满足机器人对接作业实时性和精度要求。

关键词: 机器视觉, 稳像, 机器人对接, 滤波器建模, 抖动去除

Abstract: Image stabilization is the key for accurate docking operations of robots with
vision. The whole algorithm of image stabilization is established, including images kinematics
model, KLT feature pixels detecting, SAD feature pixels matching and filters. Kalman and FIR
filters are designed for smoothing images motion parameters and built in MATLAB. Simulation of
filter of motion un-intended parameters is implemented to indicate removing jitter effect. Kalman
filter is compared with FIR filter. Comparison curves and tables are given , which demonstrate
that Kalman filter is better than FIR in robot vision image stabilization process. Based on VC++
and OpenCV, image stabilization software is programmed, and experiments are completed on
double moving robots docking operation platform. The algorithm running time is less than the
sampling period, and the precision and real-time demands are contented.

Key words: machine vision, image stabilization, robot docking, filter modeling, jitter
removing