Auditory Evidence Grids

Abstract

Sound source localization on a mobile robot can be a difficult task due to a variety of problems inherent to a real environment, including robot ego-noise, echoes, and the transient nature of ambient noise. As a result, source localization data are often very noisy and unreliable. In this work, we overcome some of these problems by combining the localization evidence over a variety of robot poses using an evidence grid. The result is a representation that localizes the pertinent objects well over time, can be used to filter poor localization results, and may also be useful for global re-localization from sound localization results.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA462871

Entities

People

  • Alan Schultz
  • Eric Martinson

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Acquisition
  • Algorithms
  • Ambient Noise
  • Artificial Intelligence
  • Background Noise
  • Cross Correlation
  • Data Acquisition
  • Data Fusion
  • Errors
  • Measurement
  • Microphones
  • Military Research
  • Noise
  • Robotics
  • Robots
  • Sensor Fusion

Fields of Study

  • Computer science

Readers

  • Acoustical Oceanography.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Auditory Neuroscience/Auditory Physiology.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy