DETECTION OF HIDDEN EDGES AND CORNERS IN SLAM-BASED INDOOR POINT CLOUDS

Abstract

Mobile mapping systems are commonly used for surveying buildings. The acquisition of the buildings’ indoor spaces with laser scanning or photogrammetry generates data in the form of point clouds. These point clouds are often used to create a model of those buildings, but so far with a low degree of automation. To automate this process, it is important to extract geometric information about corners, edges, and planes from unorganized indoor point clouds. In an indoor scenario consisting of several rooms including furniture and other objects, a point cloud is expected to show occlusions. Therefore, the detection of hidden corners and edges is of importance. In this work one approach based on contour point clouds and one approach based on planes are examined for the detection of corners and edges. Both approaches use RANSAC to extract either straight lines or planes. Through their intersection, edges and corners are determined. To examine the influence of the data quality on the results, the approaches are applied to and evaluated on different datasets of the same area of a building, which are captured by various measurement methods, including mobile mapping systems and terrestrial laser scanning. Therefore, we are creating a ground truth for parts of the building to evaluate the completeness and correctness of the corner detection. The approach based on planes presents itself to be more reliable in noisy and incomplete point clouds. The approach based on contour point clouds indicates advantages in terms of the complexity of a building´s indoor geometry.