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One of the fundamental tasks of robotics is to solve the localization problem, in which a robot must determine its true pose without any knowledge 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 this scenario, that consists in a hybridization of probabilistic and interval approaches, aiming to overcome the weaknesses found in each approach and improve the precision of results. In this paper, we use the set inversion via interval analysis (SIVIA) technique to reduce the region of uncertainty about robot localization, and a particle filter to refine the estimates. With the information provided by SIVIA, the distribution of particles can be concentrated in regions of higher interest. We compare this approach with a previous hybrid approach using contractors instead of SIVIA. Experiments with simulated data show that our hybrid method using SIVIA provides more accurate results than the method using contractors.

Place recognition is an essential task in many robotics applications. Recognizing if the robot is crossing an already visited place may be used to improve its localization and map estimation. A place recognition strategy must be as accurate as possible, despite the challenges related to environment dynamicity. It should avoid generating false positives since even a few erroneous matches may be enough to cause the degradation of the SLAM process. We propose a novel approach for place recognition inspired by interval analysis theory. Our approach models the known world as a set of intervals based on the robot's observations. The search to determine whether the current robot location is new or known begins as the robot explores its surroundings. Our approach has three main steps. First, it selects a set of nearest neighbors based on the similarity between the current robot observation and the intervals composing the known world. In the second step, our approach uses temporal con-R. Neuland