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.
Sensors (Basel, Switzerland)
|June 8, 2018
Summary
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.
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.
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