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This paper presents a novel approach to mobile robot localization using sonar sensors. This approach is based on the use of particle filters. Each particle is augmented with local environment information which is updated during the mission execution. An experimental characterization of the sonar sensors used is provided in the paper. A probabilistic measurement model that takes into account the sonar uncertainties is defined according to the experimental characterization. The experimental results quantitatively evaluate the presented approach and provide a comparison with other localization strategies based on both the sonar and the laser. Some qualitative results are also provided for visual inspection.

This paper presents a novel approach to localize an underwater mobile robot based on scan matching using a Mechanically Scanned Imaging Sonar (MSIS). When used to perform scan matching, this sensor presents some problems such as significant uncertainty in the measurements or large scan times, which lead to a motion induced distortion. This paper presents the uspIC, which deals with these problems by adopting a probabilistic scan matching strategy and by defining a method to strongly alleviate the motion induced distortion. Experimental results evaluating our approach and comparing it to previously existing methods are provided.

This paper presents a probabilistic framework to perform scan matching localization using standard time-offlight ultrasonic sensors. Probabilistic models of the sensors as well as techniques to propagate the errors through the models are also presented and discussed. A method to estimate the most probable trajectory followed by the robot according to the scan matching and odometry estimations is also presented. Thanks to that, accurate robot localization can be performed without the need of geometric constraints. The experiments demonstrate the robustness of our method even in the presence of large amounts of noisy readings and odometric errors.

Scan matching algorithms have been extensively used in the last years to perform mobile robot localization. Although these algorithms require dense and accurate sets of readings with which to work, such as the ones provided by laser range finders, different studies have shown that scan matching localization is also possible with sonar sensors. Both sonar and laser scan matching algorithms are usually based on the ideas introduced in the ICP (Iterative Closest Point) approach. In this paper a different approach to scan matching, the Likelihood Field based approach, is presented. Three scan matching algorithms based on this concept, the non filtered sNDT (sonar Normal Distributions Transform), the filtered sNDT and the LF/SoG (Likelihood Field/Sum of Gaussians), are introduced and analyzed. These algorithms are experimentally evaluated and compared to previously existing ICP-based algorithms. The obtained results suggest that the Likelihood Field based approach compares favorably with algorithms from the ICP family in terms of robustness and accuracy. The convergence speed, as well as the time requirements, are also experimentally evaluated and discussed.