Automatic Extraction of Indoor Structural Information from Point Clouds

Dongyang Cheng,Di Tian,Junchao Zhang,Dangjun Zhao,Jianlai Chen

doi:10.3390/rs13234930

Abstract

We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground plane, which is regarded as the new Cartesian plane. Second, to reduce the complexity of data processing, the data are projected into two dimensions and transformed into a binary image via the operation of an improved radius outlier removal (ROR) filter. Third, a traditional thinning algorithm is adopted to extract the image skeleton. Then, we propose a method for calculating slope through the nearest neighbor point. Moreover, the line is represented with the slopes to obtain information pertaining to the interior planes. Finally, the outline of the line is restored to a three-dimensional structure. The proposed method is evaluated in multiple scenarios, and the results show that the method is accurate (the maximum error of 0.03 m was in three scenarios) in indoor environments.

Highlights

In recent years, LiDAR technology has developed rapidly due to its high accuracy, low cost, portability, and wide application range such as in autonomous driving [1,2,3,4,5], military fields [6,7,8,9], aerospace [10,11], and three-dimensional (3D) reconstruction [12,13,14]
The point cloud from the laser scanner is precise and the point cloud that makes up the plane is projected vertically as a straight line, which results in great convenience in the subsequent processing
We propose lineline extraction method based on the nearest neighbor slope, and introduce this extraction method based on the nearest neighbor slope, and introduce this method in detail

Summary

Introduction

LiDAR (light detection and ranging) technology has developed rapidly due to its high accuracy, low cost, portability, and wide application range such as in autonomous driving [1,2,3,4,5], military fields [6,7,8,9], aerospace [10,11], and three-dimensional (3D) reconstruction [12,13,14]. With the maturation of indoor navigation [19] technology, it is important to obtain precise building interior structures from the point cloud for accurate navigation. It is difficult and time consuming to extract indoor structures from the disorder and high density [20] of point clouds, and the complexity of data processing is greatly increased due to the noise caused by the algorithm and the scanning environment. The point cloud from the laser scanner is precise and the point cloud that makes up the plane is projected vertically as a straight line, which results in great convenience in the subsequent processing. It is expensive and takes more time to measure. In some large-scale scenes, the data measured by LiDAR are integrated by a scan registration algorithm such as

Methods

Results

Discussion

Conclusion