Door State Recognition Method for Wall Reconstruction from Scanned Scene in Point Clouds

Abstract

Doors are important elements of building façades in scanned point clouds. Accurate door detection is a critical step in building reconstruction and indoor navigation. However, recent door detection methods may often obtain incomplete information and can only detect doors with a single state (open or closed). To improve this, a door state recognition method is proposed based on corner detection and straight-line fitting. Firstly, plane segmentation based on local features is introduced to obtain structural division from the raw scanned data to extract the wall. Next, the bounding box of each plane is calculated to obtain the corner points, which is then combined with the feature constraint to classify the elements of door and wall. Then, the boundary of each plane is extracted by normal vector, and the disordered and discontinuous boundary points are straight-line fitted based on projection. Finally, the state of the door is obtained through analysis of the angle between the straight-lines of the wall and the door. The effectiveness of the proposed method is tested and evaluated on the Livingroom of ICL-NUIM and House of Room detection datasets. Furthermore, comparative experimental results indicate that our method can extract corner points and recognize the different states of doors effectively and robustly in different scenes.