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Robust Stereo Visual Inertial Navigation System Based on Multi-Stage Outlier Removal in Dynamic Environments.

Dinh Van Nam1, Kim Gon-Woo1

  • 1Intelligent Robotics Laboratory, Department of Control and Robot Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju-si 28644, Chungbuk, Korea.

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Summary
This summary is machine-generated.

This study introduces a robust Visual-Inertial Navigation System (VINS) for autonomous mobile robots. It enhances navigation accuracy in dynamic indoor environments by fusing stereo camera and Inertial Measurement Unit (IMU) data with advanced outlier removal techniques.

Keywords:
bayes filteringextended kalman filtervision aided-inertial navigation systemvisual SLAMvisual-inertial odometry

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Autonomous mobile robot navigation relies on mapping and odometry.
  • Proprioceptive sensors and ego-motion estimation suffer from drift and inaccuracies in dynamic, indoor environments due to object and lighting interference.
  • Existing systems struggle with dynamic elements and sensor limitations.

Purpose of the Study:

  • To develop a robust, tightly-coupled Visual-Inertial Navigation System (VINS) for autonomous mobile robots.
  • To improve state estimation accuracy and robustness in dynamic indoor environments.
  • To mitigate the impact of dynamic objects and sensor noise on navigation.

Main Methods:

  • Implemented a Multi-State Constraint Kalman Filter (MSCKF) framework for tightly-coupled VINS.
  • Utilized a stereo camera and an Inertial Measurement Unit (IMU) for sensor fusion.
  • Developed multi-stage outlier removal strategies based on estimated state feedback to handle dynamic objects.

Main Results:

  • The proposed VINS demonstrated robust and accurate state estimation in dynamic indoor environments.
  • Achieved superior performance compared to state-of-the-art approaches in terms of accuracy and robustness.
  • Exhibited low computational complexity, making it efficient for mobile robot applications.

Conclusions:

  • The developed VINS effectively bounds navigation error by fusing complementary sensor data (stereo camera and IMU).
  • Multi-stage outlier removal significantly enhances performance in dynamic environments.
  • The system offers a practical and efficient solution for reliable autonomous navigation.