A Survey of the Simultaneous Localization and Mapping (Slam) Based On Rgb-D Camera
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Zhifan Zhang, Mengna Liu, Chen Diao, and Shengyong Chen
In recent years, the simultaneous localization and mapping (slam) have received increasing attention from computer vision and robotics, and multitudinous of results have been proposed. This paper gives a review of the slam framework base on rgb-d camera. Then, the paper provides insight into the developments on slam issues, such as visual odometry, back-end optimization and, loop closing, to address the major limitations still facing the rgb-d slam. Some latest results on the slam based on the rgb-d camera are also summarized. Finally, some conclusions are drawn, and several future research hot spots highlighted.
Survey, Computer Vision, Rgb-D Camera, Slam, Visual Odometry