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LIG-SLAM: A Lightweight Visual RGB-D SLAM for Indoor Dynamic Environments Leveraging Instance Segmentation and

Xingyu Chen1, Jiasai Wu1, Junjie Hou1

  • 1School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100091, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

LIG-SLAM enhances visual Simultaneous Localization and Mapping (SLAM) for dynamic indoor environments. This resource-efficient system significantly reduces trajectory errors by incorporating dynamic object perception and optimized inference.

Keywords:
deep learningdynamic environmentgeometric constraintstarget detectionvisual-SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional visual SLAM struggles in dynamic indoor environments due to moving objects violating scene rigidity.
  • Performance degradation and tracking instability are common issues in current SLAM systems when encountering motion.

Purpose of the Study:

  • To develop LIG-SLAM, a resource-efficient visual SLAM system robust to dynamic indoor scenes.
  • To enhance localization accuracy and real-time performance in challenging environments with moving objects.

Main Methods:

  • Utilized YOLOv5 for pixel-level dynamic object segmentation.
  • Incorporated epipolar geometric constraints for accurate dynamic feature selection.
  • Implemented RANSAC-based depth consistency checks for enhanced accuracy.
  • Applied ONNX-based optimization for accelerated inference.

Main Results:

  • Achieved over 90% reduction in Absolute Trajectory Error (ATE) compared to ORB-SLAM3 on TUM dynamic datasets.
  • Demonstrated superior accuracy and real-time performance over existing semantic SLAM approaches.
  • Significantly improved localization robustness in indoor dynamic scenarios.

Conclusions:

  • LIG-SLAM offers a robust and efficient solution for visual SLAM in dynamic indoor environments.
  • The integration of dynamic object perception and optimized inference is key to overcoming limitations of traditional SLAM.
  • The proposed system represents a significant advancement for real-world applications requiring reliable localization amidst motion.