RGB-D SLAM in Dynamic Environments Using Static Point Weighting

Shile Li, Dongheui Lee

Research output: Contribution to journalArticlepeer-review

228 Scopus citations

Abstract

We propose a real-time depth edge based RGB-D SLAM system for dynamic environment. Our visual odometry method is based on frame-to-keyframe registration, where only depth edge points are used. To reduce the influence of dynamic objects, we propose a static weighting method for edge points in the keyframe. Static weight indicates the likelihood of one point being part of the static environment. This static weight is added into the intensity assisted iterative closest point (IAICP) method to perform the registration task. Furthermore, our method is integrated into a SLAM (Simultaneous Localization and Mapping) system, where an efficient loop closure detection strategy is used. Both our visual odometry method and SLAM system are evaluated with challenging dynamic sequences from the TUM RGB-D dataset. Compared to state-of-the-art methods for dynamic environment, our method reduces the tracking error significantly.

Original languageEnglish
Article number7972984
Pages (from-to)2263-2270
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume2
Issue number4
DOIs
StatePublished - Oct 2017
Externally publishedYes

Keywords

  • Computer vision for other robotic applications
  • SLAM (Simultaneous Localization and Mapping)
  • visual tracking

Fingerprint

Dive into the research topics of 'RGB-D SLAM in Dynamic Environments Using Static Point Weighting'. Together they form a unique fingerprint.

Cite this