RGB-D SLAM in Indoor Planar Environments With Multiple Large Dynamic Objects

Ran Long, Christian Rauch, Tianwei Zhang, Vladimir Ivan, Tin Lun Lam, Sethu Vijayakumar

Research output: Contribution to journalArticlepeer-review

Abstract

This work presents a novel dense RGB-D SLAM approach for dynamic planar environments that enables simultaneous multi-object tracking, camera localisation and background reconstruction. Previous dynamic SLAM methods either rely on semantic segmentation to directly detect dynamic objects; or assume that dynamic objects occupy a smaller proportion of the camera view than the static background and can, therefore, be removed as outliers. With the aid of camera motion prior, our approach enables dense SLAM when the camera view is largely occluded by multiple dynamic objects. The dynamic planar objects are separated by their different rigid motions and tracked independently. The remaining dynamic non-planar areas are removed as outliers and not mapped into the background. The evaluation demonstrates that our approach outperforms the state-of-the-art methods in terms of localisation, mapping, dynamic segmentation and object tracking. We also demonstrate its robustness to large drift in the camera motion prior.
Original languageEnglish
Pages (from-to)8209-8216
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number3
Early online date24 Jun 2022
DOIs
Publication statusPublished - 7 Jul 2022

Keywords

  • SLAM
  • visual tracking
  • sensor fusion

Fingerprint

Dive into the research topics of 'RGB-D SLAM in Indoor Planar Environments With Multiple Large Dynamic Objects'. Together they form a unique fingerprint.

Cite this