NDLI: The combination of SfM and monocular SLAM

Content Provider	IEEE Xplore Digital Library
Author	Haoyin Zhou Tao Zhang
Copyright Year	2014
Description	Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Haoyin Zhou; Tao Zhang)
Abstract	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.
Starting Page	5282
Ending Page	5286
File Size	6269626
Page Count	5
File Format	PDF
ISBN	9781479937073
e-ISBN	9781479937080
DOI	10.1109/CCDC.2014.6853123
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Visualization Simultaneous localization and mapping Navigation Conferences structure from motion (SfM) Cameras monocular simultaneous localization and mapping (monocular SLAM) combination
Content Type	Text
Resource Type	Article

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