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IRDC-Net: Lightweight Semantic Segmentation Network Based on Monocular Camera for Mobile Robot Navigation.

Thai-Viet Dang1, Dinh-Manh-Cuong Tran1, Phan Xuan Tan2

  • 1Department of Mechatronics, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi 10000, Vietnam.

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Summary
This summary is machine-generated.

This study presents a lightweight semantic segmentation model for real-time mobile robot navigation. The model efficiently extracts corridor scenes, enabling precise obstacle avoidance and optimal path planning.

Keywords:
computer visionmobile robotnavigationobstacle avoidancesemantic segmentation

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Mobile robot navigation relies heavily on computer vision for localization and pathfinding.
  • Environmental complexity necessitates efficient obstacle avoidance systems with high computational performance.
  • Current methods often involve complex sensor systems and significant computational load.

Purpose of the Study:

  • To develop a real-time, computationally efficient solution for extracting corridor scenes from single images for mobile robot navigation.
  • To reduce training parameters and computational costs using a lightweight semantic segmentation model and quantization techniques.
  • To enhance obstacle avoidance capabilities in mobile robots.

Main Methods:

  • Proposed a lightweight semantic segmentation model combining a Fully Convolutional Network (FCN) decoder with a MobileNetV2 encoder featuring multi-scale fusion.
  • Integrated quantization techniques to minimize computational costs and training parameters.
  • Employed the Balance Cross-Entropy loss function to address class imbalances in diverse datasets.
  • Utilized the Adam optimizer and Gaussian filters to improve segmentation performance.

Main Results:

  • The proposed model demonstrated superior performance compared to baseline methods across various datasets.
  • Achieved significant reductions in computation time while maintaining high precision.
  • The model's performance remained consistent in practical experiments with a real mobile robot.
  • Effectively supported optimal path planning and obstacle avoidance.

Conclusions:

  • The lightweight semantic segmentation model offers an effective real-time solution for mobile robot navigation in complex environments.
  • The integration of quantization and specific loss functions enhances computational efficiency and segmentation accuracy.
  • The model's practical applicability was validated, enabling efficient obstacle avoidance and path planning for mobile robots.