一种改进ORB特征匹配的半稠密三维重建ORB-SLAM算法

A Semi-Dense 3D Reconstruction ORB-SLAM Algorithm with Improved ORB Feature Matching

视觉SLAM技术的目标是建立完善的地图及估计更精确的相机位姿。为了构建更加详细完整的三维地图,文中提出一种改进ORB特征匹配的半稠密三维重建ORB-SLAM算法来实现环境稀疏三维点云地图的构建。所提方法在ORB-SLAM算法的基础上,增加半稠密建图线程,建立半稠密三维点云地图,利用SURF特征匹配算法尺度不变性的特点改进了ORB特征匹配。在TUM RGBD数据集中进行的仿真实验结果表明,与ORB-SLAM算法相比,采用改进ORB-SLAM算法建立的三维地图能更加直观地显示出环境中物体的轮廓,特征匹配精度也有所增加。最后,应用两组数据集验证了算法的一致性。

The goal of visual SLAM technology is to build a more complete map and estimate a more accurate camera pose.To construct a more detailed and complete 3D map,a semi-dense 3D reconstruction ORB-SLAM algorithm with improved ORB feature matching is proposed to realize the construction of a sparse 3D point cloud map of the environment.On the basis of the ORB-SLAM algorithm,a semi-dense mapping thread is added to establish a semi-dense 3D point cloud map.Then,the scale invariance of SURF feature matching algorithm is used to improve ORB feature matching.Simulation experiments in the TUM RGBD datasets reveal that the 3D map established by the improved ORB-SLAM algorithm can more intuitively exhibit the contours of objects in the environment when compared with the ORB-SLAM algorithm,and the feature matching accuracy is increased.Finally,the consistency of the algorithm is verified by experiments in two datasets.