3D Registration of Indoor Point Clouds for Augmented Reality

Abstract

For interactive visualization in AR devices, feature descriptors of point clouds ( as-designed model and as-built model) are corresponded and registered. However, point cloud of indoor environment has lots of similar feature descriptors ( e. g., indoor scene with similar doors and windows), which leads to many false correspondences and affect registration accuracy. This paper proposes a random sample consensus ( RANSAC)-based false correspondence rejection to compute accurate transformation for the registration of such 3D point clouds. Point cloud data is collected from rooms and a hallway of a campus building, and transformation accuracy for the registration of those point clouds is tested. The results show that RANSAC-based false correspondence rejection gives transformation accuracy of 0.017 radians and 0.1924 meters in aligning two point cloud models, and hence the proposed registration approach of a model point cloud with scene point cloud may provide a foundation to accurately implement the AR on a construction jobsite.