Invisibility: A moving-object removal approach for dynamic scene modelling using RGB-D camera

Yuxiang Sun, Ming Liu, Max Q.H. Meng

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

3 Citations (Scopus)

Abstract

Scene modelling is of great importance for robots in unknown environments. Existing Visual Simultaneous Localization and Mapping (Visual SLAM) approaches are able to build impressive scene models using RGB-D cameras in static scenes. In dynamic scenes, however, moving objects can be recorded as spurious objects, which contaminates the resulting scene models. In order to build clear scene models, we propose a novel moving-object removal approach for scene modelling algorithms in this paper. Our approach does not rely on prior knowledge, such as appearance features or initial segmentation. In addition, the proposed approach does not require an initialization process, which is different from most background subtraction algorithms. The experimental results demonstrate that our approach is able to effectively remove moving objects and assist scene modelling algorithms to build clear models in dynamic scenes.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
PublisherIEEE
Pages50-55
Number of pages6
ISBN (Electronic)9781538637418
DOIs
Publication statusPublished - 23 Mar 2018
Event2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017 - Macau, China
Duration: 5 Dec 20178 Dec 2017

Publication series

Name2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
Volume2018-January

Conference

Conference2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
Country/TerritoryChina
CityMacau
Period5/12/178/12/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization
  • Modelling and Simulation

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