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A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments.

Weifeng Chen1,2, Guangtao Shang2, Kai Hu2

  • 1College of Mechanical and Electronic Engineering, Quanzhou University of Information Engineering, Quanzhou 362000, China.

Micromachines
|November 24, 2022
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Summary
This summary is machine-generated.

This study introduces a novel monocular SLAM algorithm for dynamic environments. It effectively filters dynamic features using semantic and geometric constraints, improving pose accuracy and running speed compared to existing methods.

Keywords:
Mask R-CNNORB-SLAM2SLAMdynamicoptical flow

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Area of Science:

  • Computer Vision
  • Robotics
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Visual SLAM systems typically require static environments, limiting their real-world applications.
  • Dynamic objects pose significant challenges to the robustness and accuracy of SLAM systems.
  • Existing methods relying solely on semantic or geometric information struggle to effectively filter dynamic features.

Purpose of the Study:

  • To propose a novel monocular SLAM algorithm capable of operating effectively in dynamic environments.
  • To enhance the accuracy and robustness of SLAM systems by addressing the interference from dynamic objects.
  • To improve the practical usability of SLAM in complex, real-world scenarios with moving elements.

Main Methods:

  • An adjusted Mask R-CNN is employed to initially identify and remove highly dynamic objects.
  • Feature-point pairs are matched using optical flow, and a fundamental matrix is computed.
  • Polar geometric constraints are utilized to filter out remaining dynamic feature points, refining the map.

Main Results:

  • The proposed algorithm effectively filters feature points associated with dynamic targets.
  • Experimental results demonstrate improved pose estimation accuracy in dynamic environments, particularly with high indoor dynamics.
  • The method outperforms ORB-SLAM2 in accuracy and shows a higher running speed than DynaSLAM.

Conclusions:

  • The developed monocular SLAM algorithm successfully handles dynamic environments by integrating semantic and geometric filtering.
  • The approach significantly enhances localization accuracy and operational efficiency for SLAM systems.
  • This work contributes to advancing the capabilities of SLAM for practical applications in cluttered and dynamic settings.