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

Scona R, Jaimez M, Petillot YR, Fallon M, Cremers D (2018)

Publication Type: Conference contribution

Publication year: 2018

Journal

Proceedings - IEEE International Conference on Robotics and Automation Institute of Electrical and Electronics Engineers

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 3849-3856

Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation

Event location: Brisbane, QLD, AUS

ISBN: 9781538630815

DOI: 10.1109/ICRA.2018.8460681

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.

Involved external institutions

Heriot-Watt University GB United Kingdom (GB) Technische Universität München (TUM) DE Germany (DE) University of Oxford GB United Kingdom (GB)

How to cite

APA:

Scona, R., Jaimez, M., Petillot, Y.R., Fallon, M., & Cremers, D. (2018). StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 3849-3856). Brisbane, QLD, AUS: Institute of Electrical and Electronics Engineers Inc..

MLA:

Scona, Raluca, et al. "StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments." Proceedings of the 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, QLD, AUS Institute of Electrical and Electronics Engineers Inc., 2018. 3849-3856.

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