Mobile robots need to represent obstacles in their surroundings, even moving ones, to make right movement decisions. For higher autonomy the robot should automatically build such representation from its sensory input. This paper compares the dynamic character of several gridmap building techniques: probabilistic, fuzzy, theory of evidence and histogramic. Two criteria are defined to rank such dynamism in the representation: time to show a new obstacle and time to show a new hole. The update rules for first three such techniques hold associative property which confers them static character, inconvenient for dynamic environments. Major contribution of this paper is the introduction of two new approaches are presented to improve the perception of mobile obstacles: one uses a differential equation to update the map and another uses majority voting in a limited memory per cell. Their dynamisms are also evaluated and the results presented.