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Underwater Dense Mapping with the First Compact 3D Sonar

Chinmay Burgul, Yewei Huang, Michalis Chatzispyrou, Ioannis Rekleitis, Alberto Quattrini Li, Marios Xanthidis

TL;DR

The paper investigates the first use of a compact 3D sonar (3D-15) for underwater mapping and localization, addressing visibility challenges that limit vision-based autonomy. It introduces a camera–sonar calibration workflow, characterizes material-dependent sonar responses, and develops a SLAM pipeline that fuses sonar with visual-inertial data, including loop closures and pose-graph optimization. Through field trials in caves, docks, and confined spaces, the approach achieves dense 3D reconstructions over hundreds of meters and demonstrates robustness to multipath in acoustically challenging environments. The work provides new datasets and a foundation for robust underwater 3D acoustic mapping, with practical implications for autonomous exploration and cave mapping under limited visibility.

Abstract

In the past decade, the adoption of compact 3D range sensors, such as LiDARs, has driven the developments of robust state-estimation pipelines, making them a standard sensor for aerial, ground, and space autonomy. Unfortunately, poor propagation of electromagnetic waves underwater, has limited the visibility-independent sensing options of underwater state-estimation to acoustic range sensors, which provide 2D information including, at-best, spatially ambiguous information. This paper, to the best of our knowledge, is the first study examining the performance, capacity, and opportunities arising from the recent introduction of the first compact 3D sonar. Towards that purpose, we introduce calibration procedures for extracting the extrinsics between the 3D sonar and a camera and we provide a study on acoustic response in different surfaces and materials. Moreover, we provide novel mapping and SLAM pipelines tested in deployments in underwater cave systems and other geometrically and acoustically challenging underwater environments. Our assessment showcases the unique capacity of 3D sonars to capture consistent spatial information allowing for detailed reconstructions and localization in datasets expanding to hundreds of meters. At the same time it highlights remaining challenges related to acoustic propagation, as found also in other acoustic sensors. Datasets collected for our evaluations would be released and shared with the community to enable further research advancements.

Underwater Dense Mapping with the First Compact 3D Sonar

TL;DR

The paper investigates the first use of a compact 3D sonar (3D-15) for underwater mapping and localization, addressing visibility challenges that limit vision-based autonomy. It introduces a camera–sonar calibration workflow, characterizes material-dependent sonar responses, and develops a SLAM pipeline that fuses sonar with visual-inertial data, including loop closures and pose-graph optimization. Through field trials in caves, docks, and confined spaces, the approach achieves dense 3D reconstructions over hundreds of meters and demonstrates robustness to multipath in acoustically challenging environments. The work provides new datasets and a foundation for robust underwater 3D acoustic mapping, with practical implications for autonomous exploration and cave mapping under limited visibility.

Abstract

In the past decade, the adoption of compact 3D range sensors, such as LiDARs, has driven the developments of robust state-estimation pipelines, making them a standard sensor for aerial, ground, and space autonomy. Unfortunately, poor propagation of electromagnetic waves underwater, has limited the visibility-independent sensing options of underwater state-estimation to acoustic range sensors, which provide 2D information including, at-best, spatially ambiguous information. This paper, to the best of our knowledge, is the first study examining the performance, capacity, and opportunities arising from the recent introduction of the first compact 3D sonar. Towards that purpose, we introduce calibration procedures for extracting the extrinsics between the 3D sonar and a camera and we provide a study on acoustic response in different surfaces and materials. Moreover, we provide novel mapping and SLAM pipelines tested in deployments in underwater cave systems and other geometrically and acoustically challenging underwater environments. Our assessment showcases the unique capacity of 3D sonars to capture consistent spatial information allowing for detailed reconstructions and localization in datasets expanding to hundreds of meters. At the same time it highlights remaining challenges related to acoustic propagation, as found also in other acoustic sensors. Datasets collected for our evaluations would be released and shared with the community to enable further research advancements.
Paper Structure (15 sections, 5 equations, 12 figures, 1 table)

This paper contains 15 sections, 5 equations, 12 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Experimental Setup and Analysis
  4. Sonar Characterization and Material Response
  5. Calibrating the extrinsic parameters
  6. Method Description
  7. 3D Sonar Scan Registration
  8. 3D Sonar Loop-Closure and Pose Graph Optimization
  9. Sonar SLAM and Fusion Approach
  10. Experimental Validation
  11. Dense Pointcloud Reconstruction
  12. Reconstruction of the Cave System
  13. Operation in Acoustically Challenging Environments
  14. Lessons Learned, Future Work, and Conclusions
  15. Acknowledgments

Figures (12)

  • Figure 1: 3D sonar map reconstruction in a pier piling environment with limited camera visibility. (a) Pier piling environment, (b) single 3D sonar scan and camera visibility, and (c) reconstructed pointcloud results.
  • Figure 2: Our customized BlueROV2 setup (a) and the experiment environment (b).
  • Figure 3: The sonar response on different materials commonly found in underwater environments and used for calibration.
  • Figure 4: Calibration setup. (a) Camera image of the four cinder blocks showing the top-surface borders marked in green and detected corners in blue. (b) Pointcloud from the Sonar 3D-15 displaying detected cinder blocks in yellow with borders highlighted in green.
  • Figure 5: Rays from the camera projected through 16 cinder block corners in the 2D image plane to their corresponding 3D points. Sonar 3D corner points are annotated in black and visual points in red. The camera frame $\{C\}$ and sonar frame $\{S\}$ are also illustrated in the figure using the calibrated extrinsic transformation.
  • ...and 7 more figures