This paper addresses the simultaneous localization and mapping problem in an environment where indistinguishable landmarks can be detected. A set theoretic approach to the problem is presented. Algorithms for measurement-to-feature matching, estimation of landmark positions, estimation of robot location and heading are derived in terms of uncertainty regions, under the hypothesis that errors affecting all sensor measurements are unknown-but-bounded. The proposed technique is validated in an experimental setup.