Robots that are fast and low cost
Robots that are fast and low cost
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.
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.
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.
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