Technology
Robots that are fast and low cost
Research
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
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Hongdong Li
Chief Investigator, Algorithms & Architecture Program Leader
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Australian National University
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Tom Drummond
Monash University Node Leader and Chief Investigator, AA1 Project Leader
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Monash University
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Viorela Ila
Research Fellow, AA2 Project Leader
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Australian National University
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Peter Corke
Centre Director, Chief Investigator, QUT Node Leader, AA3 Project Leader
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Queensland University of Technology
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Vincent Lui
Research Fellow
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Monash University
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William Chamberlain
PhD researcher
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Queensland University of Technology
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Steve Martin
Research Engineer
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Queensland University of Technology
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Richard Hartley
Chief Investigator
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Australian National University
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Hongdong Li
Chief Investigator, Algorithms & Architecture Program Leader
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Australian National University
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Ian Reid
Deputy Director, Semantic Representations Program Leader, University of Adelaide Node Leader and Chief Investigator
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University of Adelaide
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Andrew Davison
Partner Investigator
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Imperial College London
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Frank Dellaert
Partner Investigator
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Georgia Tech
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Marc Pollefeys
Partner Investigator
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ETH Zurich
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Yasir Latif
Research Fellow
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University of Adelaide
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Feras Dayoub
Research Fellow
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Queensland University of Technology
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Mina Henein
PhD researcher
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Australian National University
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Thanuja Dharmasiri
PhD researcher
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Monash University
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Andrew Spek
PhD researcher
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Monash University
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Yi “Joey” Zhou
PhD researcher
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Australian National University
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Sean McMahon
PhD researcher
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Queensland University of Technology
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John Skinner
PhD researcher
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Queensland University of Technology
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Niko Sünderhauf
Research Fellow, SR3 Project Leader
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Queensland University of Technology
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Trung Pham
Research Fellow
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University of Adelaide
Projects
AA1: VOS-distributed robotic vision
Ongoing
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.
AA2: ACRV SLAM Framework
Ongoing
Viorela Ila, Richard Hartley, Hongdong Li, Tom Drummond, Ian Reid, Andrew Davison, Frank Dellaert, Marc Pollefeys, Yasir Latif, Vincent Lui, Feras Dayoub, Mina Henein, Thanuja Dharmasiri, Andrew Spek, Yi “Joey” Zhou, 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.
AA3: Computer graphics simulation for robotic vision
Ongoing
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.
Australian Centre for Robotic Vision
2 George Street Brisbane, 4001
+61 7 3138 7549