Beijing Advanced Innovation Center for Imaging Technology, College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China.
Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O. Box 217, 7514 AE Enschede, The Netherlands.
Sensors (Basel). 2018 Jun 5;18(6):1838. doi: 10.3390/s18061838.
Indoor space subdivision is an important aspect of scene analysis that provides essential information for many applications, such as indoor navigation and evacuation route planning. Until now, most proposed scene understanding algorithms have been based on whole point clouds, which has led to complicated operations, high computational loads and low processing speed. This paper presents novel methods to efficiently extract the location of openings (e.g., doors and windows) and to subdivide space by analyzing scanlines. An opening detection method is demonstrated that analyses the local geometric regularity in scanlines to refine the extracted opening. Moreover, a space subdivision method based on the extracted openings and the scanning system trajectory is described. Finally, the opening detection and space subdivision results are saved as point cloud labels which will be used for further investigations. The method has been tested on a real dataset collected by ZEB-REVO. The experimental results validate the completeness and correctness of the proposed method for different indoor environment and scanning paths.
室内空间细分是场景分析的一个重要方面，为许多应用提供了必要的信息，如室内导航和疏散路线规划。到目前为止，大多数提出的场景理解算法都是基于整个点云的，这导致了复杂的操作、高的计算负载和低的处理速度。本文提出了通过分析扫描线来高效提取开口（如门和窗）位置和细分空间的新方法。演示了一种开口检测方法，该方法通过分析扫描线中的局部几何规则来细化提取的开口。此外，还描述了一种基于提取的开口和扫描系统轨迹的空间细分方法。最后，将开口检测和空间细分结果保存为点云标签，用于进一步的研究。该方法已经在 ZEB-REVO 采集的真实数据集上进行了测试。实验结果验证了该方法在不同室内环境和扫描路径下的完整性和正确性。
