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A Robust Parallel Initialization Method for Monocular Visual-Inertial SLAM.

Min Zhong1, Yiqing Yao1, Xiaosu Xu1

  • 1Key Laboratory of Micro-Inertial Instruments and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.

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|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a robust visual-inertial SLAM initialization method by combining optical flow and feature-based techniques. The novel approach enhances mapping information retention and portability, outperforming existing methods in specific scenarios.

Keywords:
SLAMinitializationmonocularvisual-inertial

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Visual-Inertial SLAM (vSLAM) systems require robust initialization for accurate state estimation.
  • Traditional initialization methods can be sensitive to environmental conditions and lack portability.
  • Integrating optical flow and feature-based methods offers complementary strengths for improved initialization.

Purpose of the Study:

  • To enhance the initialization robustness and adaptability of visual-inertial SLAM.
  • To develop a parallel initialization strategy combining optical flow and feature-based approaches.
  • To improve the portability of vSLAM systems through online extrinsic parameter estimation.

Main Methods:

  • A parallel initialization method performing simultaneous optical flow inertial and monocular feature-based initialization.
  • Joint optimization of state estimation results using bundle adjustment post-initialization.
  • Online estimation of extrinsic parameters for enhanced portability.

Main Results:

  • The proposed method retains more mapping information, leading to greater adaptability.
  • Initialization map accuracy is comparable to ORB-SLAM3 in monocular inertial mode.
  • Demonstrated superior portability compared to ORB-SLAM3 due to online extrinsic parameter estimation.

Conclusions:

  • The developed parallel initialization method significantly improves robustness and adaptability in vSLAM.
  • The approach offers comparable accuracy and enhanced portability, making it a valuable advancement.
  • Experimental validation on the EuRoC dataset confirms the method's effectiveness and robustness.