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Research on Inter-Frame Feature Mismatch Removal Method of VSLAM in Dynamic Scenes.

Zhiyong Yang1,2,3,4, Yang He3,4, Kun Zhao3,4

  • 1Engineering Research and Design Institute of Agricultural Equipment, Hubei University of Technology, Wuhan 430068, China.

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Summary
This summary is machine-generated.

This study introduces GMS-ATRANSAC to improve visual SLAM accuracy by effectively removing feature mismatches in dynamic scenes, enhancing robot localization and mapping. The method significantly reduces errors and processing time compared to existing techniques.

Keywords:
GMSVSLAMfeature matchingimproved RANSAC

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Visual Simultaneous Localization and Mapping (VSLAM) is crucial for robot pose estimation.
  • Feature point mismatches in VSLAM degrade localization and mapping accuracy, especially in dynamic environments.
  • Existing methods struggle with accurate feature matching in scenes with moving objects.

Purpose of the Study:

  • To propose and evaluate a novel method for removing inter-frame feature mismatches in dynamic scenes for visual SLAM.
  • To enhance the robustness and accuracy of mobile robot self-localization and mapping.

Main Methods:

  • Introduced Grid-based Motion Statistics (GMS) for rapid coarse screening of mismatched features.
  • Developed Adaptive Error Threshold RANSAC (ATRANSAC) for accurate mismatch removal based on internal matching rate.
  • Combined GMS and ATRANSAC into the GMS-ATRANSAC method for robust performance in dynamic and static scenes.

Main Results:

  • GMS-ATRANSAC effectively removes feature mismatches on moving objects.
  • Achieved average error reduction of 29.4% (vs. RANSAC) and 32.9% (vs. GMS-RANSAC).
  • Reduced error variance by 63.9% (vs. RANSAC) and 58.0% (vs. GMS-RANSAC), with processing time reductions of 78.3% and 38%, respectively.
  • Validated effectiveness in ORB-SLAM2 initialization and ORB-SLAM3 tracking threads.

Conclusions:

  • The proposed GMS-ATRANSAC method significantly improves feature mismatch removal in dynamic visual SLAM.
  • This leads to more accurate robot localization and mapping, outperforming RANSAC and GMS-RANSAC.
  • The algorithm enhances the reliability of VSLAM systems like ORB-SLAM2 and ORB-SLAM3.