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A Method for Reconstructing Background from RGB-D SLAM in Indoor Dynamic Environments.

Quan Lu1, Ying Pan1, Likun Hu1

  • 1School of Electrical Engineering, Guangxi University, Nanning 530004, China.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

This study presents a novel approach for visual Simultaneous Localization and Mapping (SLAM) in dynamic environments. By eliminating moving objects, it enhances camera pose estimation and improves 3D map accuracy.

Keywords:
3D reconstructingcamera poseindoor dynamic environmentskeyframesrandomized fernsvisual SLAM

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Dynamic elements in visual Simultaneous Localization and Mapping (SLAM) challenge camera pose estimation and map accuracy.
  • Existing SLAM methods struggle with the presence of moving objects in indoor environments.

Purpose of the Study:

  • To propose a robust approach for eliminating dynamic elements and reconstructing static backgrounds in indoor dynamic environments.
  • To enhance the accuracy of camera pose estimation and 3D scene reconstruction in challenging environments.

Main Methods:

  • Exploiting geometric residuals to detect and remove dynamic elements.
  • Reconstructing the static background through image repair.
  • Estimating camera pose based on the static background.
  • Utilizing randomized ferns for keyframe selection, loop closure detection, and relocalization.
  • Performing 3D scene reconstruction.

Main Results:

  • Successfully eliminated dynamic elements from indoor environments.
  • Achieved accurate static background reconstruction.
  • Demonstrated improved camera pose estimation accuracy.
  • Validated the method's effectiveness on TUM and BONN datasets, showing enhanced map reconstruction accuracy.

Conclusions:

  • The proposed method effectively addresses the challenges posed by dynamic environments in visual SLAM.
  • Accurate 3D scene reconstruction is achievable even in the presence of moving objects.
  • The approach offers a significant improvement in map reconstruction accuracy for dynamic indoor scenes.