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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Space Subdivision in Indoor Mobile Laser Scanning Point Clouds Based on Scanline Analysis.

Yi Zheng1,2, Michael Peter3, Ruofei Zhong4

  • 1Beijing Advanced Innovation Center for Imaging Technology, College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China. 2150902027@cnu.edu.cn.

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This study introduces efficient scanline analysis for indoor space subdivision. The novel method accurately detects openings like doors and windows, improving scene understanding for navigation and planning.

Keywords:
indoor point cloudsopening detectionspace subdivisiontrajectory

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Area of Science:

  • Computer Vision
  • Robotics
  • 3D Scene Analysis

Background:

  • Indoor space subdivision is crucial for applications like navigation and evacuation planning.
  • Current scene understanding methods using whole point clouds are computationally intensive and slow.
  • Efficiently identifying openings (doors, windows) and segmenting spaces remains a challenge.

Purpose of the Study:

  • To develop novel, efficient methods for indoor space subdivision using scanline analysis.
  • To accurately detect openings (doors, windows) and subdivide indoor environments.
  • To provide labeled point cloud data for further scene understanding research.

Main Methods:

  • Analyzing scanlines to detect geometric regularity for opening identification.
  • Developing a space subdivision method utilizing detected openings and scanning trajectory.
  • Saving results as point cloud labels for subsequent analysis.

Main Results:

  • Demonstrated an effective opening detection method based on local geometric regularity in scanlines.
  • Successfully implemented a space subdivision method using detected openings and scanning path.
  • Validated the method's completeness and correctness on real-world data from a ZEB-REVO scanner.

Conclusions:

  • The proposed scanline-based approach offers an efficient alternative for indoor space subdivision.
  • The method accurately identifies openings and subdivides spaces in diverse indoor environments.
  • The generated point cloud labels facilitate further advancements in indoor scene analysis.