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Semantic Mapping for Autonomous Subsea Intervention.

Guillem Vallicrosa1, Khadidja Himri1, Pere Ridao1

  • 1Underwater Robotics Research Center (CIRS), Computer Vision and Robotics Institute (VICOROB), Universitat de Girona, Parc Científic i Tecnològic de la UdG. C/Pic de Peguera 13, 17003 Girona, Spain.

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|October 26, 2021
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Summary
This summary is machine-generated.

This study introduces a novel semantic mapping method for autonomous underwater vehicle intervention tasks. It integrates simultaneous localization and mapping (SLAM) with 3D object recognition to create detailed pipe structure maps.

Keywords:
3D object recognitionAUVBayesian probabilitiesglobal descriptorsinspection, maintenance and repairlaser scannerpipeline detectionpoint cloudssemantic informationsemantic segmentationunderwater environment

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Area of Science:

  • Robotics
  • Computer Vision
  • Autonomous Systems

Background:

  • Autonomous underwater vehicles (AUVs) require precise navigation and environmental understanding for intervention tasks.
  • Mapping submerged man-made structures like pipes is challenging due to sensor limitations and dynamic environments.

Purpose of the Study:

  • To develop a method for building a semantic map to aid AUVs in autonomous intervention tasks within submerged pipe structures.
  • To enhance the accuracy and robustness of AUV navigation and object recognition in complex underwater environments.

Main Methods:

  • Integration of feature-based simultaneous localization and mapping (SLAM) with 3D object recognition.
  • Utilizing Doppler velocity log (DVL), pressure, and attitude and heading reference system (AHRS) sensors for navigation.
  • Employing laser scanners for real-time 3D point cloud acquisition and Bayesian techniques for object class estimation.

Main Results:

  • Generation of a consistent, drift-less map of the pipe structure.
  • Accurate identification and localization of pipes, valves, elbows, and tees.
  • Improved object recognition through fusion of observations and pipe connectivity information.

Conclusions:

  • The proposed semantic mapping method enables autonomous intervention tasks for AUVs in submerged pipe structures.
  • The system provides a precise semantic map, crucial for future high-level manipulation commands.
  • This approach enhances AUV capabilities for inspection and maintenance in underwater infrastructure.