Sensor Modeling for Underwater Localization Using a Particle Filter

Area of Science:

• Robotics
• Sensor Fusion
• Underwater Navigation

Background:

• Underwater sensor data is prone to uncertainties affecting localization accuracy.
• Reliable perception is crucial for autonomous underwater vehicle (AUV) navigation.

Purpose of the Study:

• To develop a framework for processing, modeling, and fusing underwater sensor signals.
• To enhance underwater localization in structured environments despite signal uncertainties.

Main Methods:

• Utilizing uncertain modeling and multi-sensor fusion techniques.
• Employing cameras and range sensors for environmental feature modeling.
• Implementing a Sequential Monte Carlo (SMC) method for localization.

Main Results:

• The framework provides a reliable environment representation for underwater navigation.
• Demonstrated improved localization accuracy compared to dead-reckoning systems.
• Successfully filtered outliers and inconsistencies in sensor observations.

Conclusions:

• The proposed framework effectively addresses uncertainties in underwater sensor data.
• It enables robust and accurate underwater localization in real-world scenarios.
• Sensor fusion is key to achieving reliable perception for submerged navigation.