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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Tom Drummond
Monash University Node Leader, Chief Investigator
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Monash University
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Peter Corke
Centre Director, Chief Investigator, QUT Node Leader, Project Leader (Manipulation Demonstrator)
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Queensland University of Technology
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Hongdong Li
Chief Investigator
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Australian National University
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Richard Hartley
Chief Investigator
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Australian National University
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Ian Reid
Deputy Director, University of Adelaide Node Leader, Chief Investigator, Project Leader (Scene Understanding)
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University of Adelaide
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Matthew Dunbabin
Chief Investigator
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Queensland University of Technology
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Niko Sünderhauf
Chief Investigator, Project Leader (Robotic Vision Evaluation Testbed)
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Queensland University of Technology
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Viorela Ila
Research Fellow
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Australian National University
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Laurent Kneip
Research Affiliate
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ShanghaiTech
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Trung Pham
Former Research Fellow
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Nvidia, Santa Clara
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Vincent Lui
Former Research Fellow, Monash University
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Sentient Vision Systems
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Feras Dayoub
Research Fellow
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Queensland University of Technology
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Yasir Latif
Research Fellow
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University of Adelaide
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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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Mina Henein
PhD researcher
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Australian National University
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Andrew Spek
Research Assistant
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Monash 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 (currently on leave)
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Queensland University of Technology
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Jun Zhang
Associated PhD researcher
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Australian National University
Projects
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.
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.
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.
Australian Centre for Robotic Vision
2 George Street Brisbane, 4001
+61 7 3138 7549