Pose-Graph SLAM using Forward-Looking Sonar

Abstract

This paper reports on a real-time simultaneous localization and mapping (SLAM) algorithm for an underwater robot using an imaging forward-looking sonar (FLS) and its application in the area of autonomous underwater ship hull inspection. The proposed algorithm overcomes specific challenges associated with deliverable underwater acoustic SLAM, including feature sparsity and false-positive data association when utilizing sonar imagery. Advanced machine learning technique is used to provide saliency aware loop closure proposals. A more reliable data association approach using different available constraints is also developed. Our evaluation is presented on real-world data collected in a ship hull inspection application, which illustrates the systems performance and robustness.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2018
Accession Number
AD1100243

Entities

People

  • Jie Li
  • Matthew Johnson-roberson
  • Michael Kaess
  • Ryan M. Eustice

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Autonomy
  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Autonomous Navigation
  • Autonomous Underwater Vehicles
  • Computer Science
  • Coordinate Systems
  • Data Association
  • Dead Reckoning
  • Detection
  • Electrical Engineering
  • Geometry
  • Global Positioning Systems
  • Inertial Measurement Units
  • Marine Engineering
  • Measurement
  • Motion Planning
  • Navigation
  • Neural Networks
  • Robots
  • Ship Hulls
  • Simultaneous Localization And Mapping
  • Sonar Images
  • Statistical Analysis
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy