While recent advancements in using sophisticated onboard sensors on robotic platforms have made it possible to consign various tasks like exploration, mapping, and flocking to teams of mobile robots, some issues like handling extensive amount of data, high dependency on sensors’ performance, and high expenses emerge. In this paper, the problem of mapping unknown environments by a team of heterogeneous mobile robots with limited and inexpensive sensing abilities is addressed. The concepts of Information Space and sensor models have been employed to plan the motions of robots with limited sensory data in order to accomplish the common goal of mapping the entire workspace as complete as possible. Also, a cooperation architecture is proposed to fuse and interrelate the dissimilar data obtained by individual heterogeneous robots and allocate various exploratory tasks to each of them in order to complete the map. The algorithm works with various limited sensing models, such as depth-limited boundary distance sensor, quadridirectional depth sensor, depth-limited gap sensor, and depth-limited radially-bounded depth senor. Based on each sensor model, the best moving strategy is introduced to maximize the workspace coverage for each robot. The proposed algorithm, which yields a geometric map of the environment, is implemented in diverse simulated problems both with and without sensing noises, and the results and comparisons with a recent related work show that it is able to reliably construct maps of simply-connected and multiply-connected environments with convex and concave obstacles. In the presence of noises, the produced maps had about 12.4% false positive and 3.3% false negative errors on average. Also, some sensitivity analyses are done on the effects of workspace size and number of robots on the mapping time.