This paper proposes Point-cloud Fast Filter (PFF), an algorithm to process efficiently 3D data from point-cloud sensors in order to build traversability maps for service robots. Our method is intended to be integrated with a 2D mapping algorithm, enhancing 2D standard maps with enough traversability information for robot navigation in indoor structured environments. The method is agnostic to the 3D sensor or mapping algorithm used, and keeps computational requirements low. Thus, we enable middle-class computers and a wide variety of sensors to be employed for service robots, reducing the costs of the platform. We evaluate the performance of PFF with different 3D sensors on a real robot and its impact on mapping, comparing it with alternative 2D and 3D mapping approaches.