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Robots that are fast and low cost

Overview


Algorithms & Architecture

This program aims to create advanced algorithms and techniques to allow computer vision to be run in real-time on robotic systems deployed in large-scale real-world applications, using distributed sensing and computation resources and to provide efficient and unified software platforms for real-time robot visual SLAM algorithms and techniques development and employment in real-world environment. There are three research projects (AA1, AA2, and AA3) under this program, each addressing a significant aspect of robotic vision research, development, and applications. AA1 (VOS) will provide a common, distributed computational platform that takes advantage of distributed sensing and computational capabilities to solve large complex robotic problems. AA2 (ACRV-SLAM) is focused on the development and integration of robot vision algorithms in robust vision, real-time vision and semantic vision areas, into a single SLAM-centred robot navigation framework. The framework will be demonstrated in real-world robot applications including AUV (autonomous underwater vehicle), UAV (unmanned aerial vehicle, or flying robot), and ground-based autonomous vehicles. AA3 (SIMUL) aims to provide a photorealistic graphics simulation environment to facilitate and accelerate the development of advanced robot vision algorithms and systems.

People


Tom Drummond
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Peter Corke
  • Peter Corke

    Centre Director, Chief Investigator, QUT Node Leader, Project Leader (Manipulation Demonstrator)

  • QUT

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Hongdong Li
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Richard Hartley
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Ian Reid
  • Ian Reid

    Deputy Director, University of Adelaide Node Leader, Chief Investigator, Project Leader (Scene Understanding)

  • University of Adelaide

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Matthew Dunbabin
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Niko Sünderhauf
  • Niko Sünderhauf

    Chief Investigator, Project Leader (Robotic Vision Evaluation Testbed)

  • Queensland University of Technology

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Viorela Ila
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Laurent Kneip
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Trung Pham
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Vincent Lui
  • Vincent Lui

    Former Research Fellow, Monash University

  • Sentient Vision Systems

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Feras Dayoub
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Yasir Latif
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William Chamberlain
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Steve Martin
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Mina Henein
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Andrew Spek
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Sean McMahon
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John Skinner
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Jun Zhang
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Projects


Robotic Vision Evaluation and Benchmarking


2018 onwards

Niko Sünderhauf, Feras Dayoub, David Hall, John Skinner, Rohan Smith, Ben Talbot

This project will develop new standardised benchmark tasks, evaluation metrics, and a new robotic vision challenge competition that can be one of the legacies of the Centre.With the new tasks, metrics, and benchmarking competitions, we aspire to recreate for robotic vision the positive effects competitions such as ILSVRC or COCO had for the advances of deep learning and computer vision. We will run our new competition annually in conjunction with a major computer vision and robotics conference.The project will combine the variety and complexity of real-world data with the flexibility of synthetic graphics and physics engines. We will put a focus on evaluating algorithms developed in other Centre projects on real robot hardware under realistic conditions, and provide valuable feedback on the robustness of the evaluated algorithms and approaches.The project aims to overcome the current lack of meaningful standardised evaluation protocols and benchmarks which is a significant roadblock for the evolution of robotic vision, and impedes reproducible and comparable research in robotic vision worldwide.

niko.suenderhauf@qut.edu.au

Previous project: AA2: ACRV SLAM Framework


- 2017

Viorela Ila, Richard Hartley, Hongdong Li, Tom Drummond, Ian Reid, Laurent Kneip, Matthew Dunbabin, Yasir Latif, Vincent Lui, Feras Dayoub, Mina Henein, Andrew Spek, Jun Zhang, Sean McMahon

This project will develop novel SLAM algorithms which can perform in challenging environments (large-scale, dynamic, dense, non-rigid). ACRV-SLAM is a common framework that integrates efficient implementations of the proposed algorithms with the goal to facilitate their distribution to the robotics community and the industrial partners, and to produce high quality demonstrators.

viorela.ila@anu.edu.au

Previous Project: AA3: Computer graphics simulation for robotic vision


- 2017

Peter Corke, John Skinner, Steve Martin, Niko Sünderhauf, Trung Pham

The performance of a robotic vision system depends on the initial state of the robot and the world it perceives as well as the lighting conditions, unforseen distractors (transient moving objects) and unrepeatable sensor noise. A consequence is that no robotic vision experiment can ever be repeated and the performance of different algorithms cannot be rigorously and quantitatively compared. For machine learning applications a critical bottleneck is the limited amount of real world image data that can be captured and labeled for both training and testing purposes.This project investigates the potential of photo-realistic graphical simulation based on state-of-the-art game-engine technology to address both these challenges.

peter.corke@qut.edu.au

Previous project: AA1: VOS-distributed robotic vision in the cloud and embedded mobile platforms


Sept 2016 - 2017

Tom Drummond, Peter Corke, Vincent Lui, William Chamberlain, Steve Martin

The goal of AA1 is to create a Vision Operating System that provides a framework for bringing together multiple sensing and computational resources to solve complex robotic vision problems. This will enable robots to make use of external sensing resources (e.g. CCTV cameras in the environment, or sensors mounted on other robots) as well as computation resources (either attached to those sensors, or provided as a large computing resource within the network). This kind of framework enables novel solutions to complex problems in which the various resources are combined collaboratively to solve complex localisation, navigation, understanding and planning problems.

tom.drummond@monash.edu

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