Algorithm designed to process the image stream of a high-speed, high-resolution camera (1Mpix at 200+ Hz) online, and provide a useful visual cue such as optical flow to the robot controller.Read Now
OpenGV is a C++ library for solving geometric computer vision problems. It contains efficient implementations of absolute-pose, relative-pose, triangulation, and point-cloud alignment methods for the calibrated case.Read Now
The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing. PCL is released under the terms of the BSD license, and is free for commerical and research use.Read Now
This project aims to develop a vision-based robotic surgical assistant for minimally invasive orthopaedics procedures. The system would be composed of a robotic arm with an attached camera-arthroscope bundle for intra-articular navigation. The system will be capable of a) localizing instruments robustly and reliably inside the human joints; b) generating dense and accurate 3D reconstructed models of the knee joint from intra-articular images,; and c) semi-autonomously navigating the camera (via visual servoing) to follow the surgeons’ tools.Read Now
A physical benchmark for robotic picking: objects, configuration, and overall benchmark rules. Challenges are an important way to drive progress but they occur only occasionally and the test conditions are difficult to replicate outside the challenge. This benchmark is motivated by experience in the recent Amazon Picking Challenge.Read Now
The open online robotics education source. University-level, short video lessons and full online courses to help you understand and prepare for this technology of the future.Read Now
Tom Drummond's notes on the use of Lie Groups and Projective Geometry for Engineering and Computer Vision.Read Now
The classic introduction to the geometric relationship between the 3D world and 2D image projections.Read Now
In this workshop, we aim to bring together researchers and experts in key areas for grasping and manipulation such as perception, control, learning, design of hands and grippers, and studies analysing human manipulation skills. We aspire to identify recent developments in these research areas, both in theory and applications, discussing recent achievements, debating underlying assumptions, and challenges for future progress.Read Now
In the late years Deep Learning has been a great force of change on most computer vision tasks. In video analysis problems, however, such as action recognition and detection, motion analysis and tracking, shallow architectures remain surprisingly competitive. What is the reason for this conundrum? Larger datasets are part of the solution.Read Now
Recent advances in deep learning techniques have made impressive progress in many areas of computer vision, including classification, detection, and segmentation. While all of these areas are relevant to robotics applications, robotics also presents many unique challenges which require new approaches.Read Now
The International Conference on Robotics and Automation (ICRA) 2018 will be held for the first time in Australia in Brisbane from the 21st to 25th May.Read Now
Deep learning supercomputer is providing the computer power to train deeper and larger models and to handle large numbers of parameters and big data.Read Now
Centre Researchers Chief Investigator Matt Dunbabin and Research Fellow Feras Dayoub won the people's choice award in the Google Impact Challenge Australia in 2016. The award is worth $750,000. Their project with the Great Barrier Reef Foundation will create a low-cost 'robo reef protector'.Read Now
The ARC Centre of Excellence for Robotic Vision is leading the world in transformational research tackling the critical and complex challenge of applying computer vision to robotics. We believe that the ability to see, to visually understand the complex world around us and respond to it, is critical for the next generation of robots that will perform useful work in agriculture, environmental monitoring, healthcare, infrastructure inspection, construction and manufacturing.
We believe that robotic vision is the key to unleashing the full potential of robots and fundamentally changing the way we live and work.
The Centre was funded in 2014 by the Australian Research Council (ARC), to not only conduct research in the exciting new field of robotic vision but to also build research capacity, develop the research and industry leaders of tomorrow, engage with the community, and to help people learn about robotics, vision and coding. We are establishing a vibrant international robotic vision community in partnership with four Australian universities (QUT, ANU, University of Adelaide and Monash), CSIRO and five international organizations (Oxford University, Imperial College London, INRIA, ETH Zurich and Georgia Tech).
The Centre is led by Director Professor Peter Corke (QUT), Deputy Director Professor Ian Reid (Adelaide), and Chief Investigators Professor Tom Drummond (Monash) and Professor Robert Mahony (ANU).
Australian Centre for Robotic Vision
2 George Street Brisbane, 4001
+61 7 3138 7549