Robust Window Detection from 3D Laser Scanner Data

In this paper we propose a robust system for window detection using popular descriptive statistics and image based methods, making use of 3D information from a laser scanner. The scanner generates 3D point clouds containing intensity and distance information in a spherical co-ordinate system, with optional additional RGB texture information. The applied descriptive statistical method exploits basic local features such as mean, variance and standard deviation of the distance measurement data. The laser distance information shows high variability in windows region, due to specular reflections on window screens on one hand, and screen penetration on the other hand. Therefore we determine an adaptive threshold on the basis of local absolute differences of adjacent laser-measured distances in the image formed by the angular co-ordinate system of the scanner. For window segmentation the image is binarized using the derived threshold, and morphological operations such as closing using adaptive (i.e. distance - dependent) structural elements are performed. After contour analysis the resulting bounding rectangles are used to retrieve the positions and global shapes of windows in the image. The system provides a sufficient windows detection rate for direct application in a deformation measurement system.