International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 991–998, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-991-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 991–998, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-991-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Aug 2020

12 Aug 2020

3D SEQUENTIAL IMAGE MOSAICING FOR UNDERWATER NAVIGATION AND MAPPING

E. Nocerino1, F. Menna2, B. Chemisky1, and P. Drap1 E. Nocerino et al.
  • 1LIS UMR 7020, Aix-Marseille Université, CNRS, ENSAM, Université De Toulon, Marseille, 13397, France
  • 23D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy

Keywords: underwater navigation, image stitching, image mosaicing, SLAM, visual odometry

Abstract. Although fully autonomous mapping methods are becoming more and more common and reliable, still the human operator is regularly employed in many 3D surveying missions. In a number of underwater applications, divers or pilots of remotely operated vehicles (ROVs) are still considered irreplaceable, and tools for real-time visualization of the mapped scene are essential to support and maximize the navigation and surveying efforts. For underwater exploration, image mosaicing has proved to be a valid and effective approach to visualize large mapped areas, often employed in conjunction with autonomous underwater vehicles (AUVs) and ROVs. In this work, we propose the use of a modified image mosaicing algorithm that coupled with image-based real-time navigation and mapping algorithms provides two visual navigation aids. The first is a classic image mosaic, where the recorded and processed images are incrementally added, named 2D sequential image mosaicing (2DSIM). The second one geometrically transform the images so that they are projected as planar point clouds in the 3D space providing an incremental point cloud mosaicing, named 3D sequential image plane projection (3DSIP). In the paper, the implemented procedure is detailed, and experiments in different underwater scenarios presented and discussed. Technical considerations about computational efforts, frame rate capabilities and scalability to different and more compact architectures (i.e. embedded systems) is also provided.