Sensor Fusion for Intelligent Behavior on Small Unmanned Ground Vehicles

Abstract

Sensors commonly mounted on small unmanned ground vehicles (UGVs) include visible light and thermal cameras, scanning LIDAR, and ranging sonar. Sensor data from these sensors is vital to emerging autonomous robotic behaviors. However, sensor data from any given sensor can become noisy or erroneous under a range of conditions, reducing the reliability of autonomous operations. We seek to increase this reliability through data fusion. Data fusion includes characterizing the strengths and weaknesses of each sensor modality and combining their data in a way such that the result of the data fusion provides more accurate data than any single sensor. We describe data fusion efforts applied to two autonomous behaviors: leader-follower and human presence detection. The behaviors are implemented and tested in a variety of realistic conditions.

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Document Details

Document Type
Technical Report
Publication Date
May 02, 2007
Accession Number
ADA494367

Entities

People

  • B. Sights
  • E. B. Pacis
  • G. Ahuja
  • G. Kogut
  • Hobart R. Everett

Organizations

  • Naval Information Warfare Systems Command

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Collision Avoidance
  • Computer Vision
  • Control Systems
  • Data Fusion
  • Detection
  • Detectors
  • Image Processing
  • Jet Propulsion
  • Robots
  • Sensor Fusion
  • Simultaneous Localization And Mapping
  • Technology Transfer
  • Unmanned Systems
  • Warning Systems

Readers

  • Computational Modeling and Simulation
  • Robotics and Automation.
  • Sensor Fusion and Tracking Systems.

Technology Areas

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