Occupancy maps offer a useful representation of the environment for applications in automated driving and robotics. If created from 3D lidar scanners, they can be very accurate. Traditional static occupancy mapping methods are not able to represent a changing environment. In this paper, we present a novel dynamic occupancy mapping (DOM) algorithm. Inspired by the phase congruency idea from computer vision, it has an intuitive formulation and yet is effective in practice. In addition, our framework provides solutions to several common challenges during occupancy mapping, such as multi-lidar fusion and ground estimation. Finally, we use several experiments to quantitatively evaluate the quality of DOM's output and our algorithm runs in real-time (10Hz).