A dense semantic mapping system based on CRF-RNN network

Jiyu Cheng, Yuxiang Sun, Max Q.H. Meng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

16 Citations (Scopus)


Geometric structure and appearance information of environments are main outputs of Visual Simultaneous Localization and Mapping (Visual SLAM) systems. They serve as the fundamental knowledge for robotic applications in unknown environments. Nowadays, more and more robotic applications require semantic information in visual maps to achieve better performance. However, most of the current Visual SLAM systems are not equipped with the semantic annotation capability. In order to address this problem, we develop a novel system to build 3-D Visual maps annotated with semantic information in this paper. We employ the CRF-RNN algorithm for semantic segmentation, and integrate the semantic algorithm with ORB-SLAM to achieve the semantic mapping. In order to get real-scale 3-D visual maps, we use the RGB-D data as the input of our system. We test our semantic mapping system with our self-generated RGB-D dataset. The experimental results demonstrate that our system is able to reliably annotate the semantic information in the resulting 3-D point-cloud maps.

Original languageEnglish
Title of host publication2017 18th International Conference on Advanced Robotics, ICAR 2017
Number of pages6
ISBN (Electronic)9781538631577
Publication statusPublished - 30 Aug 2017
Event18th International Conference on Advanced Robotics, ICAR 2017 - Hong Kong, China
Duration: 10 Jul 201712 Jul 2017

Publication series

Name2017 18th International Conference on Advanced Robotics, ICAR 2017


Conference18th International Conference on Advanced Robotics, ICAR 2017
CityHong Kong

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Mechanical Engineering
  • Control and Optimization

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