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A Motion Segmentation Dynamic SLAM for Indoor GNSS-Denied Environments.

Yunhao Wu1, Ziyao Zhang2,3, Haifeng Chen1

  • 1College of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China.

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Summary

OS-SLAM enhances Simultaneous Localization and Mapping (SLAM) in dynamic environments by using optical flow motion segmentation. This robust system significantly reduces errors in GPS-denied settings.

Keywords:
GNSS-denied environmentsdynamic sceneoptical flowsemantic seg-mentationsimultaneous localization and mapping

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial in GPS-denied environments.
  • Dynamic objects and variables negatively impact SLAM positional accuracy.
  • Creating static-element maps in dynamic scenes is a key challenge.

Purpose of the Study:

  • To develop a robust SLAM system for dynamic environments.
  • To improve positional accuracy and map consistency in GNSS-deprived settings.
  • To reduce the impact of dynamic objects on visual SLAM.

Main Methods:

  • Introduced OS-SLAM, integrating optical flow motion segmentation.
  • Developed a lightweight multi-scale optical flow network for accurate motion segmentation.
  • Proposed a YOLO-fastest and Rigidmask fusion approach to handle non-rigid objects.
  • Generated static dense point cloud maps by filtering abnormal point clouds.

Main Results:

  • OS-SLAM demonstrated enhanced robustness in dynamic environments.
  • The system significantly reduced the impact of dynamic objects on localization.
  • Experimental results showed substantial reductions in Absolute Position Error (APE) and Relative Position Error (RPE) compared to ORB-SLAM3 on the TUM dataset.

Conclusions:

  • OS-SLAM effectively integrates optical flow and motion segmentation for robust SLAM.
  • The proposed fusion method mitigates mis-segmentation errors caused by dynamic objects.
  • OS-SLAM offers superior performance in dynamic visual SLAM tasks, especially in challenging GNSS-deprived scenarios.