基于ORB-SLAM2系统的快速误匹配剔除算法与地图构建  

Fast mismatch elimination algorithm and map-building based on ORB-SLAM2 system

作　　者：席志红[1] 王洪旭 韩双全 XI Zhihong;WANG Hongxu;HAN Shuangquan(College of Information and Communication Engineering,Harbin Engineering University,Harbin Heilongjiang 150001,China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院,哈尔滨150001

出　　处：《计算机应用》2020年第11期3289-3294,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60875025)。

摘　　要：针对ORB-SLAM2系统中随机抽样一致(RANSAC)算法在误匹配剔除时因其算法本身的随机性而导致效率较低的问题和在ORB-SLAM2系统里未能构建稠密点云地图的问题,采用渐进一致采样(PROSAC)算法来改进ORB-SLAM2系统中的误匹配剔除,并在系统中添加稠密点云地图和八叉树地图构建线程。首先,与RANSAC算法相比,PROSAC算法依据评价函数对特征点进行预排序,并选取评价质量较高的特征点求解单应性矩阵,根据单应性矩阵的解与匹配误差阈值进行误匹配剔除;然后,根据ORB-SLAM2系统进行相机的位姿估计与重定位;最后,根据所选关键帧进行稠密点云地图与八叉树地图的构建。根据TUM数据集上的实验结果,PROSAC算法在进行相同图像的误匹配剔除时所用时间是RANSAC算法的50%左右,并且所提系统的绝对轨迹误差与相对位姿误差与ORB-SLAM2系统基本一致,表现出良好的鲁棒性;另外,与稀疏点云地图相比,提出的新构建地图可以直接用于机器人的导航与路径规划。To address the problem that the RANdom SAmple Consensus(RANSAC)algorithm in the ORB-SLAM2 system has a low efficiency due to the randomness of the algorithm when eliminating mismatches and fails to build dense point cloud map in ORB-SLAM2 system,a PROgressive SAmple Consensus(PROSAC)algorithm was adopted to improve the mismatch elimination in the ORB-SLAM2 system and the dense point cloud map and the octree map building threads were added in this system.Firstly,compared with RANSAC algorithm,in PROSAC algorithm,the feature points were preordered according to the evaluation function,and the feature points with high evaluation quality were selected to solve the homography matrix.According to the solution of the homography matrix and the matching error threshold,the mismatches were eliminated.Secondly,the pose estimation and relocation of the camera were carried out according to the ORB-SLAM2 system.Finally,the dense point cloud map and the octree map were constructed according to the selected key frames.According to the experimental results on TUM dataset,PROSAC algorithm took about 50%time to perform the mismatch elimination of the same images compared to RANSAC algorithm,and the proposed system had the absolute trajectory error and relative pose error basically consistent with the ORB-SLAM2 system,showing good robustness.Besides,compared with the sparse point cloud map,the proposed new maps could be directly used for robot navigation and path planning.

关 键 词：同步定位与地图构建 随机抽样一致算法 渐进一致采样算法 稠密点云地图 八叉树地图 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]