Visual and Visual-Inertial SLAM: State of the Art, Classification, and Experimental Benchmarking
نویسندگان
چکیده
Simultaneous Localization and Mapping is now widely adopted by many applications, researchers have produced very dense literature on this topic. With the advent of smart devices, embedding cameras, inertial measurement units, visual SLAM (vSLAM), visual-inertial (viSLAM) are enabling novel general public applications. In context, paper conducts a review popular approaches with focus vSLAM/viSLAM, both at fundamental experimental levels. It starts structured overview existing vSLAM viSLAM designs continues new classification dozen main state-of-the-art methods. A chronological survey viSLAM’s development highlights historical milestones presents more recent methods into classification. Finally, performance experimentally assessed for use case pedestrian pose estimation handheld device in urban environments. The five open-source Vins-Mono, ROVIO, ORB-SLAM2, DSO, LSD-SLAM compared using EuRoC MAV dataset corresponding to navigation. detailed analysis computation results identifies strengths weaknesses each method. Globally, ORB-SLAM2 appears be most promising algorithm address challenges navigation, tested two datasets.
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ژورنال
عنوان ژورنال: Journal of Sensors
سال: 2021
ISSN: ['1687-725X', '1687-7268']
DOI: https://doi.org/10.1155/2021/2054828