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  11. Let-se2-vins: A Hybrid Optical Flow Framework For Robust Visual-inertial Slam

LET-SE2-VINS: A Hybrid Optical Flow Framework for Robust Visual-Inertial SLAM

Wei Zhao1, Hongyang Sun1,2, Songsong Ma1

  • 1School of Mechanical and Electrical Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China.

Sensors (Basel, Switzerland)
|July 12, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

SE2-LET-VINS enhances visual-inertial simultaneous localization and mapping (VI-SLAM) using a neural network for feature extraction and SE2 optical flow tracking. This improves localization accuracy and robustness in challenging environments like low lighting and rapid motion.

Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) systems are crucial for autonomous navigation.
  • Existing frameworks like VINS-Mono face challenges in accuracy and robustness in complex environments.

Purpose of the Study:

  • To enhance the VINS-Mono framework for improved localization accuracy and robustness.
  • To develop a hybrid system integrating neural network-based feature extraction and SE2 optical flow tracking.

Main Methods:

  • Integration of Lightweight Neural Network (LET-NET) for feature extraction.
  • Implementation of Special Euclidean Group in 2D (SE2) for optical flow tracking.
  • Utilizing IMU and camera data with pre-integration and RANSAC for feature matching.
Keywords:
VI-SLAMdeep learningenhance localization accuracyhybrid optical flow method

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Main Results:

  • Achieved up to 43.89% improvement in localization accuracy on the EuRoc dataset with loop closure.
  • Demonstrated error reductions of 29.7%, 21.8%, and 24.1% in no-loop scenarios (MH_04, MH_05, V2_03).
  • Experimental results confirm superior robustness and accuracy through trajectory visualization and analysis.

Conclusions:

  • SE2-LET-VINS provides a robust and accurate solution for visual-inertial navigation.
  • The system shows significant performance gains in challenging environments.
  • Paves the way for advanced real-time autonomous applications.
key point detection