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SLAM Algorithm for Mobile Robots Based on Improved LVI-SAM in Complex Environments.

Wenfeng Wang1,2, Haiyuan Li1, Haiming Yu1,3

  • 1College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China.

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Summary
This summary is machine-generated.

This study introduces a novel multi-sensor fusion Simultaneous Localization and Mapping (SLAM) method. It enhances robot navigation in complex environments, improving trajectory accuracy and robustness compared to existing SLAM techniques.

Keywords:
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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Sensor Fusion

Background:

  • Robot autonomous movement relies on accurate localization and mapping, often achieved through Simultaneous Localization and Mapping (SLAM).
  • Single-sensor SLAM methods struggle with accuracy and robustness in complex, dynamic environments due to issues like degeneracy.
  • Existing SLAM frameworks require enhancement for reliable performance in challenging real-world scenarios.

Purpose of the Study:

  • To propose a multi-sensor fusion SLAM method to overcome the limitations of single-sensor approaches in complex environments.
  • To enhance feature point detection and loop-closure detection for improved localization and mapping accuracy.
  • To validate the proposed method's performance against established SLAM frameworks in diverse datasets and simulated environments.

Main Methods:

  • Developed a multi-sensor fusion SLAM approach building upon the LVI-SAM framework.
  • Integrated the SuperPoint algorithm for advanced feature point extraction from visual-inertial data, improving detection in challenging conditions.
  • Employed scan context optimization to enhance the performance of loop-closure detection in complex environments.

Main Results:

  • Achieved a 12% and 11% reduction in Root Mean Square Error (RMSE) for trajectory estimation on the KITTI (05 sequence) and M2DGR (Street07 sequence) datasets, respectively, compared to LVI-SAM.
  • Demonstrated reduced trajectory errors at starting and ending points in simulated complex animal farm environments compared to LVI-SAM.
  • Experimental results confirm superior precision and robustness in localization and mapping tasks within complex environments.

Conclusions:

  • The proposed multi-sensor fusion SLAM method significantly enhances localization and mapping accuracy and robustness.
  • The integration of SuperPoint for feature detection and scan context for loop closure proves effective in complex scenarios.
  • This approach offers a promising solution for reliable robot navigation in challenging, real-world environments.