Multibeam Data Processing for Underwater Mapping

From archaeology to the inspection of subsea structures, underwater mapping has become critical to many applications. Because of the balanced trade-off between range and resolution, multibeam sonars are often used as the primary sensor in underwater mapping platforms. These sonars output an image representing the intensity of the received acoustic echos over space, which must be classified into free and occupied regions before range measurements are determined and spatially registered. Most classifiers found in the underwater mapping literature use local thresholding techniques, which are highly sensitive to noise, outliers, and sonar artifacts typically found in these images. In this paper we present an overview of some of the techniques developed in the scope of our work on sonar-based underwater mapping, with the aim of improving map accuracy through better segmentation performance. We also provide experimental results using data collected with a DIDSON imaging sonar that show that these techniques improve both segmentation accuracy and robustness to outliers.

Authors

• Pedro Vaz Teixeira
• Franz S. Hover
• John J. Leonard
• Michael Kaess

Keywords

• Sonar measurements
• Robot sensing systems
• Acoustic beams
• Image segmentation
• Mathematical model
• Acoustics
• Bathymetry
• Over Space
• Segmentation Performance
• Multibeam Echosounder
• Receiver Operating Characteristic Curve
• Decrease In Intensity
• Intensity Distribution
• Intensity Measurements
• Mixture Model
• Exponential Distribution
• Kullback-Leibler
• Beampattern
• Images In Set
• Sidelobe
• Class Position
• Pose Estimation
• Maximum A Posteriori
• Markov Random Field
• Angular Resolution
• Simultaneous Localization And Mapping
• Rayleigh Distribution
• Range Bin
• One-dimensional Array
• Mapping Pipeline
• Transmission Loss
• Conditional Distribution
• Set Of Values
• Types Of Images
• Acoustic Signals
• Fish Schools