Wide Aperture Imaging Sonar Reconstruction using Generative Models | IEEE Conference Publication | IEEE Xplore

Wide Aperture Imaging Sonar Reconstruction using Generative Models


Abstract:

In this paper we propose a new framework for reconstructing underwater surfaces from wide aperture imaging sonar sequences. We demonstrate that when the leading object ed...Show More

Abstract:

In this paper we propose a new framework for reconstructing underwater surfaces from wide aperture imaging sonar sequences. We demonstrate that when the leading object edge in each sonar image can be accurately triangulated in 3D, the remaining surface may be “filled in” using a generative sensor model. This process generates a full three-dimensional point cloud for each image in the sequence. We propose integrating these surface measurements into a cohesive global map using a truncated signed distance field (TSDF) to fuse the point clouds generated by each image. This allows for reconstructing surfaces with significantly fewer sonar images and viewpoints than previous methods. The proposed method is evaluated by reconstructing a mock-up piling structure and a real world underwater piling, in a test tank environment and in the field, respectively. Our surface reconstructions are quantitatively compared to ground-truth models and are shown to be more accurate than previous state-of-the-art algorithms.
Date of Conference: 03-08 November 2019
Date Added to IEEE Xplore: 28 January 2020
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Conference Location: Macau, China

References

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