A Robust Keyframe-Based Visual SLAM for RGB-D Cameras in Challenging Scenarios

Xi Lin, Yewei Huang, Dingyi Sun, Tzu Yuan Lin, Brendan Englot, Ryan M. Eustice, Maani Ghaffari

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The accuracy of RGB-D SLAM systems is sensitive to the image quality, and can be significantly compromised in adverse situations such as when input images are blurry, lacking in texture features, or overexposed. In this paper, based on Continuous Direct Sparse Visual Odometry (CVO), we present a novel Keyframe-based CVO (KF-CVO) with intrinsic keyframe selection mechanism that effectively reduces the tracking error. We then extend KF-CVO to a RGB-D SLAM system, CVO SLAM, equipped with place recognition via ORB features, and joint bundle adjustment & pose graph optimization. Comprehensive evaluations on publicly available benchmarks show that the proposed RGB-D SLAM system achieves a higher success rate than current state-of-the-art-methods. The proposed system is more robust to difficult benchmark sequences than current state-of-the-art methods, where adverse situations such as rapid camera motions, environments lacking in texture, and overexposed images when strong illumination exists.

Original languageEnglish
Pages (from-to)97239-97249
Number of pages11
JournalIEEE Access
Volume11
DOIs
StatePublished - 2023

Keywords

  • RGB-D camera
  • Visual SLAM
  • indoor environments

Fingerprint

Dive into the research topics of 'A Robust Keyframe-Based Visual SLAM for RGB-D Cameras in Challenging Scenarios'. Together they form a unique fingerprint.

Cite this