This paper deals with the simultaneous localization and mapping problem (SLAM) for a robot. The robot has to build a map of its environment while localizing itself using an the partially built map. It is assumed that (i) the map is made of punctual static landmarks, (ii) the landmarks are indistinguishable, (iii) the only exteroceptive measurements correspond to the distance between the robot and the landmarks. This problem can be takled using Monte-Carlo approaches, but the resulting method is often trapped in some local minimimum from which it is difficult to escape without solving the data association problem. The main contribution of this paper is to show that, due to the redundancy of measurements, a constraint-based approach can find a guaranteed enclosure of the global solution with a polynomial complexity. A simulated test-case is provided to illustrate the efficiency of the approach.