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Robots that see in all conditions

Overview


Robust Vision

The Robust Vision program develops robotic vision algorithms and novel vision hardware to enable robots to see and act in all viewing conditions. The program is developing a suite of algorithms that enable robots to perceive their environments and consequently act purposefully under the incredible range of environmental conditions possible, including low light, rain, snow, ice sleet, fog, smoke, dust, wind, glare and heat. In addition, it is further developing innovative sensing hardware to facilitate robot operation under challenging viewing conditions such as low light, or through partial obscuration cameras and hyperspectral cameras.

The key question we are addressing is, how can innovations in existing computer vision and robotic vision techniques and vision sensing hardware enable robots to perform well under the wide range of challenging conditions encountered by robots and applied computer vision technology in the real world?

 

People


Michael Milford
  • Michael Milford

    Chief Investigator, Robust Vision Program Leader, RV 1 Project Leader

  • Queensland University of Technology

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Chuong Nguyen
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Hongdong Li
  • Hongdong Li

    Chief Investigator, Algorithms & Architecture Program Leader

  • Australian National University

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Chunhua Shen
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Jonathan Roberts
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Peter Corke
  • Peter Corke

    Centre Director, Chief Investigator, QUT Node Leader, AA3 Project Leader

  • Queensland University of Technology

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Jürgen “Juxi” Leitner
  • Jürgen “Juxi” Leitner

    Research Fellow, VA1 Project Leader

  • Queensland University of Technology

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Niko Sünderhauf
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Donald Dansereau
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Sean McMahon
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Sourav Garg
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James Mount
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Dorian Tsai
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Dan Richards
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Timo Stoffregen
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Projects


RV1: Robust Robotic Visual Recognition


Ongoing

Michael Milford, Hongdong Li, Jonathan Roberts, Chunhua Shen, Jürgen “Juxi” Leitner, Chuong Nguyen, Niko Sünderhauf, Sourav Garg, Sean McMahon, James Mount

Robust robotic visual recognition for adverse conditions will develop algorithms that solve the fundamental robotic tasks of place recognition and object recognition under challenging environmental conditions including darkness, weather, adverse atmospheric conditions and seasonal change, and translate them into applications in industry. This project is relatively mature and hence is now pushing more heavily towards industry outcomes and engagement than some of the other projects. The key question we are addressing is, how can existing computer vision and robotic vision techniques be innovated to enable them to perform well under the wide range of challenging conditions encountered by robots and applied computer vision technology in the real world?

m.milford@qut.edu.au

RV2: Novel visual sensing & hybrid hardware for robotic operation in adverse conditions & for difficult objects


Ongoing

Chuong Nguyen, Peter Corke, Michael Milford, Donald Dansereau, Dan Richards, Dorian Tsai, James Mount, Timo Stoffregen

Novel visual sensing for robotic operation in adverse conditions will advance the performance of robot vision algorithms by using new conventional low light cameras, as well as rotational filters, hyperspectral cameras and thermal cameras to improve robot autonomy under any viewing condition. We are looking at the development of new algorithms that exploit the particular advantages of these innovative cameras to enable new performance benchmarks in tasks such as scene understanding, place recognition and object recognition. The development of hardware-software solutions that can deal with these corner cases - reflections, transparency and low light conditions - will be applicable to all Centre projects involving visual sensing and can be used by those projects to robustify their systems for performance under adverse conditions.

chuong.nguyen@anu.edu.au

Australian Centre for Robotic Vision
2 George Street Brisbane, 4001
+61 7 3138 7549