计算机科学
尺度不变特征变换
熵(时间箭头)
特征提取
模式识别(心理学)
算法
人工智能
特征(语言学)
Orb(光学)
数据挖掘
数学
图像(数学)
语言学
量子力学
物理
哲学
作者
Dan Yin,Zhou Siwei,Wang Pengcheng,Lin Manling,Hui Song,Feng Ke,Luo Kaiqing
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:8: 127134-127141
被引量:3
标识
DOI:10.1109/access.2020.3008457
摘要
Feature points loss and images mismatch in the variation of light intensity, weak texture and large angle rotation for the feature points extraction of ORB-SLAM2 are severe. To deal with the problem, a feature points extraction algorithm based on adaptive information entropy, i.e., Adaptive Information Entropy Feature (AIEF) algorithm is proposed. According to the information entropy, the image blocks with less information are removed and those with more texture image information and larger gradient are selected. Then an adaptive algorithm is used to automatically calculate the optimal threshold of the image information entropy. The image blocks are homogenized to avoid that the extracted feature points are too dense and getting stuck is prevented, which makes the algorithm more robust. Finaly validation is performed using the Oxford standard data set and the performances of the AIEF algorithm are compared with those of the SIFT, SURF, and ORB-SLAM2 algorithms. Experimental results on the Oxford standard data set demonstrate that the AIEF algorithm outperforms the traditional counterparts in terms of processing time, number of feature points, correct matching number and correct matching rate.
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