基于四叉树法和PROSAC算法改进的视觉SLAM技术  

Improved Visual SLAM Technology Based on Quadtree Method and PROSAC Algorithm

作　　者：杜根 张志安[1] Du Gen;Zhang Zhian(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学机械工程学院,南京210094

出　　处：《兵工自动化》2024年第5期37-42,共6页Ordnance Industry Automation

摘　　要：为解决在同时定位与地图构建(simultaneous localization and mapping,SLAM)的前端进行特征点匹配时,随机抽样一致法(random sample consensus,RANSAC)存在的迭代次数高、实时性较差、鲁棒性不稳定等问题,提出一种基于四叉树法和渐进一致采样法(progressive sample consensus,PROSAC)算法融合改进的图像匹配算法。实现四叉树法+PROSAC算法的误匹配剔除算法,在EuRoC数据集上对改进后的ORB-SLAM2算法进行实验。结果表明:相比于ORB-SLAM2系统,该算法在Vicon Room 103数据集上总体绝对轨迹误差平均值减小了39.28%,总体相对位姿误差减小了35.45%,具有更高的建图精度。In order to solve the problems of random sample consensus(RANSAC),such as high number of iterations,poor real-time performance and unstable robustness in the front end of simultaneous localization and mapping(SLAM),an improved image matching algorithm based on the fusion of quadtree method and progressive sample consensus(PROSAC)algorithm is proposed.The mismatching elimination algorithm of quadtree method+PROSAC algorithm is implemented,and the improved ORB-SLAM2 algorithm is tested on EuRoC data set.The results show that compared with ORB-SLAM2 system,the proposed algorithm reduces the average absolute trajectory error by 39.28%and the relative pose error by 35.45%on Vicon Room 103 dataset,and has higher mapping accuracy.

关 键 词：四叉树编码 特征点匹配 PROSAC算法 SLAM 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]