移动机器人环境视觉图像小波稀疏压缩传感与恢复重构

Wavelet Sparsity Based Compressive Sensing and Reconstruction for Mobile Robot Environmental Vision

基于最新压缩传感技术理论,采用小波稀疏方法和正交匹配跟踪算法,对移动机器人常规环境视觉图像压缩传感存储与恢复重构进行了研究。结果表明:进行小波稀疏的压缩传感编码可以大大减少机器人环境视觉图像信息量,降低存储与传输代价,通过正交匹配追踪算法恢复的视觉图像,可以满足机器人常规环境视觉探测。

Based on the novel compressed sampling theory, compressed sensing storage and reconstruction for environment images from robot vision were researched in the paper by the help of wavelet sparsity and orthogonal matching pursuit algorithm. Experiments of several typical environment images for sparsity, compressed storage, and reconstruction were carried out. The result showed it could reduce the amount of environment images information, and lower the cost of information storage and transmission. The reconstruction images could generally satisfy the re- quirement of robot environment exploration.