This work tackles the problem of the localization of a robot in in large and cooperative environments using real-time data coming either from the robot onboard sensors or/and from the sensors in the environment. The paper focuses on the 3-DOF localization of a mobile robot that is to say the estimation of the robot coordinates (xmr, ymr, θmr) in a 2D-environment. The problem of nonlinear bounded-error estimation is viewed as a set inversion. The paper presents the theoretical formulation of the localization method in a bounded-error context and the parameter estimation based on interval analysis. Simulation results as well as real experiments show the contributions of the method. The method is able to easily integrate a large variety of sensors, from the roughest to the most complex one. The method takes into account a heterogeneous set of measurements, a flexible number of measurements, a statistical knowledge on the measurements limited to the tolerance, and the fact the measurements are acquired both from the robot onboard sensors and the environment sensors. The way that environment model inaccuracies can be taken into account is also presented.