StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments

Raluca Scona, Mariano Jaimez, Yvan R. Petillot, Maurice Fallon, Daniel Cremers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)

Abstract

Dynamic environments are challenging for visual SLAM as moving objects can impair camera pose tracking and cause corruptions to be integrated into the map. In this paper, we propose a method for robust dense RGB-D SLAM in dynamic environments which detects moving objects and simultaneously reconstructs the background structure. While most methods employ implicit robust penalisers or outlier filtering techniques in order to handle moving objects, our approach is to simultaneously estimate the camera motion as well as a probabilistic static/dynamic segmentation of the current RGB-D image pair. This segmentation is then used for weighted dense RGB-D fusion to estimate a 3D model of only the static parts of the environment. By leveraging the 3D model for frame-to-model alignment, as well as static/dynamic segmentation, camera motion estimation has reduced overall drift - as well as being more robust to the presence of dynamics in the scene. Demonstrations are presented which compare the proposed method to related state-of-the-art approaches using both static and dynamic sequences. The proposed method achieves similar performance in static environments and improved accuracy and robustness in dynamic scenes.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages3849-3856
Number of pages8
ISBN (Electronic)9781538630815
DOIs
Publication statusPublished - 13 Sep 2018
EventIEEE International Conference on Robotics and Automation 2018 - Brisbane, Australia
Duration: 21 May 201825 May 2018

Publication series

NameInternational Conference on Robotics and Automation
PublisherIEEE
ISSN (Electronic)2577-087X

Conference

ConferenceIEEE International Conference on Robotics and Automation 2018
Abbreviated titleICRA 2018
CountryAustralia
CityBrisbane
Period21/05/1825/05/18

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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  • Cite this

    Scona, R., Jaimez, M., Petillot, Y. R., Fallon, M., & Cremers, D. (2018). StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3849-3856). (International Conference on Robotics and Automation). IEEE. https://doi.org/10.1109/ICRA.2018.8460681