Sensor Modeling for Underwater Localization Using a Particle Filter

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 4; p. 1549
• Main Authors: Martínez-Barberá, Humberto, Bernal-Polo, Pablo, Herrero-Pérez, David
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.02.2021; MDPI
• Subjects: Acoustics; Cameras; Localization; multi-sensor fusion; Multisensor fusion; navigation; Navigation systems; Noise; Open source software; Outliers (statistics); Perceptions; Representations; Robotics; Sensors; Signal processing; sonar; uncertainty modeling; Underwater detectors; underwater localization; Underwater structures; underwater vehicle frameworks; Vehicles; Velocity; navigation; uncertainty modeling; sonar; underwater vehicle frameworks; underwater localization; multi-sensor fusion
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21041549

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 from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.