Sensor Modeling for Underwater Localization Using a Particle Filter.

ABSTRACT: This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized

SUBMITTER: Martinez-Barbera H 

PROVIDER: S-EPMC7926564 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature