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Asynchronous visual event-based time-to-contact.

Xavier Clady1, Charles Clercq2, Sio-Hoi Ieng1

  • 1Vision Institute, Universitée Pierre et Marie Curie, UMR S968 Inserm, UPMC, CNRS UMR 7210, CHNO des Quinze-Vingts Paris, France.

Frontiers in Neuroscience
|February 27, 2014
PubMed
Summary
This summary is machine-generated.

This study presents an event-based Time To Contact (TTC) method for fast obstacle avoidance in mobile robotics. This approach utilizes asynchronous event-based sensors for improved speed and reduced computational load compared to traditional methods.

Keywords:
computer visionevent-based computationneuromophic visionroboticstime to contact

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Technology

Background:

  • Frame-based sensing limits temporal dynamics and increases computational costs for mobile robots.
  • High-speed reactive control for mobile applications requires efficient sensing with low energy and computational demands.

Purpose of the Study:

  • To introduce a fast obstacle avoidance method for mobile robots using event-based sensing.
  • To enable high-speed sensor-based reactive control by overcoming limitations of conventional sensing paradigms.

Main Methods:

  • Developed an event-based Time To Contact (TTC) computation using visual event-based motion flows.
  • Utilized an asynchronous event-based time-encoded imaging sensor for data acquisition.
  • The method processes incoming events continuously for rapid response.

Main Results:

  • Validated the event-based TTC method on an indoor mobile robot.
  • Compared event-based TTC with laser range finder TTC, demonstrating comparable performance.
  • Showcased the potential of event-based sensing for enhanced mobile robotics.

Conclusions:

  • Event-based sensing offers a promising alternative for fast and efficient environmental perception in mobile robotics.
  • The proposed method facilitates rapid obstacle avoidance, crucial for high-speed autonomous navigation.
  • This technology opens new avenues for developing more responsive and computationally efficient robotic systems.