计算机科学
光流
计算机视觉
光圈(计算机存储器)
自适应光学
人工智能
复眼
视野
光学
声学
物理
图像(数学)
作者
Chong Shen,Xin Zhao,Xindong Wu,Huiliang Cao,Chenguang Wang,Jun Tang,Jun Liu
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-1
被引量:6
标识
DOI:10.1109/jiot.2023.3324966
摘要
Autonomous velocity measurement technology based on optical flow plays an important role in applications of the Internet of Things. However, robust velocity measurement results need to provide a robust optical flow field and distance to the ground, especially for complex ground scenes. To solve this problem, the present paper proposes a visual velocity measurement method based on a 3 × 3 camera-array multi-aperture biomimetic compound-eye imaging system. The multi-aperture optical flow field is first obtained from compound-eye through multi-scale analysis and Bayes threshold processing. The real distance to the ground is then obtained by extracting the disparity information of nine apertures for adaptive depth estimation. The velocity measurement error is less than 0.6m/s in low-altitude (8 m and 13 m) flight scenarios with multiple obstacles.
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