SEMANTIC-RTAB-MAP (SRM): A semantic SLAM system with CNNs on depth images | Zendy

Mingyuan Mao | Zendy; Hewei Zhang | Zendy; Simeng Li | Zendy; Baochang Zhang | Zendy

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SEMANTIC-RTAB-MAP (SRM): A semantic SLAM system with CNNs on depth images

Author(s) -

Mingyuan Mao,

Hewei Zhang,

Simeng Li,

Baochang Zhang

Publication year - 2019

Publication title -

mathematical foundations of computing

Language(s) - English

Resource type - Journals

ISSN - 2577-8838

DOI - 10.3934/mfc.2019003

Subject(s) - artificial intelligence , simultaneous localization and mapping , computer science , computer vision , depth map , point cloud , semantic mapping , rgb color model , deep learning , monocular , image (mathematics) , robot , mobile robot

SLAM (simultaneous localization and mapping) system can be implemented based on monocular, RGB-D and stereo cameras. RTAB-MAP is a SLAM system, which can build dense 3D map. In this paper, we present a novel method named SEMANTIC-RTAB-MAP (SRM) to implement a semantic SLAM system based on RTAB-MAP and deep learning. We use YOLOv2 network to detect target objects in 2D images, and then use depth information for precise localization of the targets and finally add semantic information into 3D point clouds. We apply SRM in different scenes, and the results show its higher running speed and accuracy.

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