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