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Garment Recognition and Reconstruction Using Object Simultaneous Localization and Mapping.

Yilin Zhang1, Koichi Hashimoto1

  • 1Department of System Information Sciences, Graduate School of Information Sciences, Tohoku University, Aoba-ku, Sendai 980-8579, Japan.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
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Robots can now better process garments using a new vision system. This system enables robots to recognize clothing and map their surroundings, improving automation in the garment industry.

Area of Science:

  • Robotics
  • Computer Vision
  • Garment Processing

Background:

  • Robotics integration in garment processing is limited due to fabric deformability.
  • Automating garment handling requires advanced recognition and spatial understanding.

Purpose of the Study:

  • To develop a vision-based model for garment recognition and environment reconstruction.
  • To facilitate the application of robots in garment processing tasks.

Main Methods:

  • Object SLAM (Simultaneous Localization and Mapping) for real-time tracking and mapping.
  • 2D instance segmentation for garment detection and a synthetic 3D mesh dataset for DeepSDF (Signed Distance Function) model enhancement.
  • CAPE (Cylinder and Plane Extraction) model integrated with SLAM for environmental plane reconstruction.
Keywords:
clothing manufacturingcomputer visiongarment reconstructioninstance segmentationobject SLAMrobotics

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Main Results:

  • Demonstrated feasibility of simultaneous garment and plane detection/reconstruction using an Intel Realsense camera.
  • Improved garment recognition via 2D instance segmentation models.
  • Enhanced garment shape understanding with the DeepSDF model.
  • Robust environment reconstruction with CAPE plane detection integrated into SLAM.

Conclusions:

  • The developed system shows potential for increased automation and efficiency in garment processing.
  • The combination of garment recognition and environment reconstruction is key for robotic applications in this industry.