One of the fundamental tasks of robotics is to solve the localization problem, in which a robot must determine its true pose without anyknowledge on its initial location. In underwater environments, this is specially hard due to sensors restrictions. For instance, many times,the localization process must rely on information from acoustic sensors, such as transponders. We propose a method to deal with thisscenario, that consists in a hybridization of probabilistic and interval approaches, aiming to overcome the weaknesses found in eachapproach and improve the precision of results. In this paper, we use the set inversion via interval analysis (SIVIA) technique to reduce theregion of uncertainty about robot localization, and a particle filter to refine the estimates. With the information provided by SIVIA, thedistribution of particles can be concentrated in regions of higher interest. We compare this approach with a previous hybrid approachusing contractors instead of SIVIA. Experiments with simulated data show that our hybrid method using SIVIA provides more accurateresults than the method using contractors.