摘要
针对机器人在复杂的室内环境中,因提取特征点低效率、高失真造成性价比较低的问题,提出一种改进的SIFT特征点提取与匹配算法,并在此基础上构建基于Kinect的SLAM系统。SLAM系统前端对SIFT特征点提取法进行改进,使用高斯分离模糊函数,提高SIFT算法提取特征点的速度,并且使用RANSAC筛选不稳定特征点。本文所提出的改进型SIFT特征点提取法的SLAM系统可以对复杂与空旷的室内环境高效率、低失真的重构。
To solve the problem of low performance-price ratio due to low efficiency and high distortion of feature points extraction in the complex indoor environment of robots,an improved SIFT feature points extraction and matching algorithm is proposed,and on this basis,a SLAM system based on Kinect is built.The SLAM system front end improves the SIFT feature point extraction method,uses the Gaussian separation fuzzy function,improves the speed of SIFT algorithm to extract the feature point,and uses RANSAC to screen unstable feature points.The SLAM system with improved SIFT feature points extraction method can reconstruct the complex and empty indoor environment with high efficiency and low distortion.
作者
陈凯扬
罗志灶
王建兴
CHEN Kai-yang;LUO Zhi-zao;WANG Jian-xing(Laboratory of Machine Vision,Minjiang University,Fuzhou 350100,China;Department of Physics and Electronic Information Engineering,Minjiang University,Fuzhou 350100,China)
出处
《计算机与现代化》
2019年第11期34-37,共4页
Computer and Modernization
基金
福建省大学生创新创业训练计划项目(201610395028)
