In this paper, we consider the problem of merging partial occupancy grid environment maps, which are extracted independently by individual robot units during search and rescue (SAR) operations in complex disaster environments. Moreover, these maps are combined using intensity-based and area-based features without knowing the initial position and orientation of the robots. Our proposed approach handles the limitation of existing studies in the literature; for example, the limited overlapped area between partial maps of robots is enough for good merging performance and unstructured and complex partial environment maps can be merged efficiently. These abilities allow multirobot teams to efficiently generate the occupancy grid map of catastrophe areas and localize buried victims in the debris as soon as possible. The simulation results support the potential of the proposed multirobot map fusion methodology for SAR operations in unstructured, complex, and completely unknown disaster environments.