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Incremental 3D Cuboid Modeling with Drift Compensation.

Masashi Mishima1, Hideaki Uchiyama2, Diego Thomas3

  • 1Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan. mishima@limu.ait.kyushu-u.ac.jp.

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This study introduces an online 3D cuboid modeling framework using RGB-D SLAM. It accurately generates CAD models from point clouds, improving detection and compensating for SLAM errors.

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Area of Science:

  • Computer Vision
  • Robotics
  • 3D Reconstruction

Background:

  • Accurate 3D modeling is crucial for robotics and augmented reality.
  • Existing methods often struggle with online processing and real-time accuracy.

Purpose of the Study:

  • To develop an incremental 3D cuboid modeling framework using RGB-D SLAM.
  • To enable accurate, online generation of cuboid CAD models from point cloud data.

Main Methods:

  • Incremental plane reconstruction from RGB-D SLAM point clouds.
  • Cuboid detection via analysis of plane relationships (orthogonality, convexity, proximity).
  • Online refinement of cuboid models and SLAM error compensation through cuboid registration.

Main Results:

  • Demonstrated accurate cuboid detection and modeling in cluttered environments.
  • Achieved online processing capabilities for 3D cuboid CAD model generation.
  • Showcased advantages over batch-based cuboid detection methods.

Conclusions:

  • The proposed framework enables efficient and accurate incremental 3D cuboid modeling.
  • It effectively integrates with RGB-D SLAM for real-time applications.
  • The method offers improved precision and recall in cuboid detection compared to traditional approaches.