For deployment in previously unknown, unstructured and GPS-denied environments, autonomous mobile rescue robots need to localize themselves in the environment and create a map of it using a simultaneous localization and mapping (SLAM) approach. While most existing lidar-based methods use occupancy grids to represent a map, the use of truncated signed distance functions (TSDFs) is investigated in this paper to improve accuracy and robustness. In contrast to occupancy grids, TSDFs represent the distance to the nearest surface in every grid cell. This enables sub-pixel precision during localization and increases the basin of convergence of scan matching. To enable consistent mapping of large spaces, an efficient branch-and-bound based loop closure detection is applied. The evaluation of the proposed approach with publicly available benchmark data shows that the proposed approach yields improved accuracy in comparison to occupancy grid based methods, while requiring similar runtime. Furthermore, it is demonstrated that the proposed approach is able to map a large scale environment with urban search and rescue elements in real-time.