Orthogonal 3D-SLAM for indoor environments using right angle corners

Abstract

Soon, in many service robotic applications, a realtime localization and 3D-mapping capability will be necessary for autonomous navigation. Toward a light and practical SLAM algorithm for indoor scenarios, we propose a fast SLAM algorithm which benefits from sensor geometry for feature extraction and enhance the mapping process using dominant orthogonality in the engineered structures of man-made environments. Range images obtained using a nodding SICK are segmented into planar patches with polygonal boundaries in linear time. Right corner features are constructed based on the recognized orthogonal planes and used for robot localization. In addition to these corners, the map also contains planar patches with inner and outer boundaries for 3D modeling and recognition of the major building structures. Experiments using a mobile robot in our laboratory hallway prove the effectiveness of our approach. Results of the algorithm are compared with hand-measured ground truth.