Urban search and rescue (USAR) is a time critical task. One goal in rescue robotics is to have a team of heterogeneous robots that explore autonomously the disaster area, while jointly creating a map of the terrain and registering victim locations, which can further be utilized by human task forces for rescue. Basically, the robots have to solve autonomously in real-time the problem of simultaneous localization and mapping (SLAM), consisting of a continuous state estimation problem and a discrete data association problem. In this paper we contribute a novel method for efficient loop closure in harsh large-scale environments that utilizes RFID technology for data association and slippage-sensitive odometry for 2D pose tracking. Furthermore, we introduce a computational efficient method for building elevation maps by utilizing an extended Kalman filter for 3D pose tracking, which can be applied in real-time while navigating on rough terrain. The proposed methods have been extensively evaluated within outdoor environments, as well as within USAR test arenas designed by the National Institute of Standards and Technology (NIST). Our results show that the proposed methods perform robustly and efficiently within the utilized benchmark scenarios. © 2007 Wiley Periodicals, Inc.