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LVID-SLAM: A Lightweight Visual-Inertial SLAM for Dynamic Scenes Based on Semantic Information.

Shuwen Wang1, Qiming Hu1, Xu Zhang1

  • 1School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

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|July 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces LVID-SLAM, a lightweight visual-inertial system for Simultaneous Localization and Mapping (SLAM) in dynamic environments. It significantly improves pose accuracy and robustness by integrating semantic object detection with Inertial Measurement Unit (IMU) data.

Keywords:
SLAMdynamic environmentgeometric constraintsmulti-sensor fusionsemantic mappingtarget detection

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for robot navigation but faces challenges in dynamic environments.
  • Existing deep learning SLAM methods are either fast but inaccurate or accurate but computationally expensive.
  • Current maps lack semantic information, limiting robot environmental understanding and task performance.

Purpose of the Study:

  • To develop a lightweight visual-inertial SLAM system capable of operating effectively in dynamic environments.
  • To enhance robot environmental understanding by integrating semantic information into the mapping process.
  • To improve pose accuracy and robustness compared to existing SLAM frameworks.

Main Methods:

  • The system, LVID-SLAM, is built upon the ORB-SLAM3 framework, incorporating a new thread for object detection.
  • It tightly couples semantic object detection with geometric information to filter dynamic features.
  • Inertial Measurement Unit (IMU) data is utilized to aid feature extraction and recover from visual tracking loss, constructing a dense octree-based semantic map.

Main Results:

  • LVID-SLAM demonstrated excellent pose accuracy and robustness in highly dynamic scenes.
  • Achieved an average Absolute Trajectory Error (ATE) reduction of over 80% compared to ORB-SLAM3 on the TUM dataset.
  • Outperformed other methods in dynamic conditions, providing both real-time performance and robustness.

Conclusions:

  • The proposed LVID-SLAM system offers a robust and efficient solution for visual-inertial SLAM in dynamic environments.
  • Integrating semantic information and IMU data significantly enhances SLAM performance.
  • The system provides a foundation for robots with improved environmental awareness and task execution capabilities.