摘要
特征点算法在图像处理领域有着重要的作用,ORB(Oriented FAST and Rotated BRIEF)特征点提取算法因具有旋转不变性、计算速度快等优点,被广泛使用。传统的ORB特征点算法在光照变换的场景中极易无法检测到特征点,且运行时间还是无法满足强实时系统。论文依据ORB特征点算法原理,使用减少检测区域面积、优化检测流程、优化三角函数求解、动态灰度阈值等方法,对原有的ORB算法进行改进,在提升ORB特征点检测算法速度的同时,提高ORB特征点检测算法在不同光照下的适用性。最后通过实验证明,改进后的ORB算法相比于OpenCV库中算法平均用时更少,且在亮度变化的场景中也可以提取到较多的特征点。改进后的算法可以更合适在视觉SLAM(Simultaneous Localization and Mapping)等实时性强,且使用环境光照易变换的系统中使用。
The feature point algorithm plays an important role in image processing.The ORB(Oriented FAST and Rotated BRIEF)feature point extraction algorithm is widely used because of its rotation invariance and fast computation speed.The traditional ORB feature point algorithm can not detect feature points easily in light-transformed scenes,and the running time is not enough for a strong real-time system.Based on the principle of ORB feature point algorithm,this paper improves the original ORB algorithm by using methods such as reducing the area of detection area,optimizing the detection process,optimizing trigonometric function solution,dynamic gray threshold,etc.It improves the speed of ORB feature point detection algorithm and improves the applicability of ORB feature point detection algorithm under different lighting conditions.Finally,experiments show that the improved ORB algorithm takes less time on average than the algorithm in OpenCV library,and can extract more feature points in scenes with varying brightness.The improved algorithm is more suitable for real-time systems such as visual SLAM(Simultaneous Localization and Mapping)and systems that use ambient light to easily transform.
作者
王新域
李月锋
邹军
WANG Xinyu;LI Yuefeng;ZOU Jun(School of Science,Shanghai Institute of Technology,Shanghai 201418)
出处
《计算机与数字工程》
2025年第12期3347-3351,3377,共6页
Computer & Digital Engineering
关键词
特征点
ORB
光照变换
动态灰度阈值
characteristic points
ORB
light-transformed
dynamic gray threshold
