摘要
本文针对ORB-SLAM2算法在黑暗环境或纹理较少的环境下提取特征点少,从而导致SLAM系统定位精度不高、匹配对数较少,进而导致系统崩溃的问题,提出了一种基于自适应阈值的特征点提取算法与改进的四叉树均匀化策略。首先基于图像的亮度进行基于自适应阈值的FAST特征点提取,之后通过改进的四叉树均匀化策略对图像的特征点进行剔除与补偿,完成特征点选取。实验结果表明,与原算法相比,改进后的特征点提取算法在黑暗环境与纹理较少的环境下,匹配对的数量提升了17.6%,SLAM轨迹精度提升了49.8%,有效的提升了SLAM系统的鲁棒性和精度。
A feature point extraction algorithm based on adaptive threshold and an improved quadtree homogenization strategy are proposed to address the issue of low positioning accuracy or low matching logarithms of the SLAM system caused by the ORB-SLAM2 algorithm extracting fewer feature points in dark environments or environments with fewer textures,resulting in system crashes.Firstly,based on the brightness of the image,FAST(Features from Accelerated Seed Test)feature points are extracted using adaptive thresholds.Then,an improved quadtree homogenization strategy is used to eliminate and compensate the feature points of the image,completing feature point selection.The experimental results show that the improved feature point extraction algorithm increases the number of matching pairs by 17.6%and SLAM trajectory accuracy by 49.8%compared to the original algorithm in dark and textured environments,effectively improving the robustness and accuracy of the SLAM system.
作者
马哲伟
周福强
王少红
Ma Zhewei;Zhou Fuqiang;Wang Shaohong(Key Laboratory of Modern Measurement and Control Technology Ministry of Education,Beijing Information Science and Technology University,Beijing 100192,China)
出处
《电子测量技术》
北大核心
2024年第6期94-99,共6页
Electronic Measurement Technology
