The combination of SfM and monocular SLAM
To realize autonomous navigation with a regular camera in an unknown environment, there are mainly two types of approaches: SfM and monocular SLAM. They both have advantages and disadvantages. SfM is slow and cannot eliminate outliers, but it is able to provide 3D information from a series of images without any additional information. Monocular SLAM can hardly work unless the initial value is close to the real value, but it is fast and can handle outliers naturally. The combination approach proposed in this paper combines SfM and monocular SLAM. It uses SfM as a observer to linearize the observation function used in monocular SLAM, and uses the results of monocular SLAM to accelerate SfM. Outliers are pointed out by SfM and handled by monocular SLAM. Simulation and experiment results show that the proposed combination approach is feasible. The accuracy is improved compared with SfM and outliers can be eliminated.
structure from motion (SfM) monocular simultaneous localization and mapping (monocular SLAM) combination
Haoyin Zhou Tao Zhang
Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing
国际会议
长沙
英文
5282-5286
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）