Finding Disaster Victims: Robot-Assisted 3D Mapping of Urban Search and Rescue Environments via Landmark Identification

Abstract

In this paper a landmark identification method is proposed for identifying large distinguishable landmarks for 3D visual simultaneous localization and mapping (SLAM) in a search and rescue environment. The novelty of the method is the utilization of both 3D (i.e., depth images) and 2D images. By utilizing a scale invariant feature transform (SIFT)-based approach and incorporating 3D depth imagery, we can use more reliable and robust recognition and matching between landmarks from multiple images for 3D mapping of the environment. Preliminary experiments utilizing the proposed method verified its ability to identify clusters of SIFT keypoints in the images for representation of potential landmarks in the scene