稳健性(进化)
计算机科学
特征提取
人工智能
匹配(统计)
Orb(光学)
模式识别(心理学)
棱锥(几何)
Blossom算法
计算机视觉
特征(语言学)
特征匹配
四叉树
理论(学习稳定性)
网格
比例(比率)
同时定位和映射
算法
图像(数学)
数学
移动机器人
机器人
机器学习
基因
量子力学
物理
哲学
语言学
几何学
统计
化学
生物化学
作者
D.Laxmana Rao,Yuhao Yuan,Xin Chen
出处
期刊:IEEE Advanced Information Technology, Electronic and Automation Control Conference
日期:2021-03-12
被引量:1
标识
DOI:10.1109/iaeac50856.2021.9390843
摘要
Based on the application of visual SLAM, an improved ORB feature extraction and matching algorithm is proposed to overcome the shortcomings of the original ORB algorithm. In order to improve the stability of the scale, a multi-scale spatial pyramid is established to detect and extract the FAST feature points of each grid in different scales, so as to improve the stability of the scale; the quadtree method is used to divide the feature points to make the distribution more uniform; after the coarse matching, the PRASAC method is used to eliminate the wrong matching points, which improves the matching accuracy. Experimental results show that the algorithm can effectively improve the matching accuracy and efficiency, and meet the requirements of robustness and real-time in visual SLAM.
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