2011 Strategic roadmap for Australian research infrastructure

The 2011 Roadmap articulates the priority research infrastructure areas of a national scale (capability areas) to further develop Australia’s research capacity and improve innovation and research outcomes over the next five to ten years. The capability areas have been identified through considered analysis of input provided by stakeholders, in conjunction with specialist advice from Expert Working Groups   It is intended the Strategic Framework will provide a high-level policy framework, which will include principles to guide the development of policy advice and the design of programs related to the funding of research infrastructure by the Australian Government. Roadmapping has been identified in the Strategic Framework Discussion Paper as the most appropriate prioritisation mechanism for national, collaborative research infrastructure. The strategic identification of Capability areas through a consultative roadmapping process was also validated in the report of the 2010 NCRIS Evaluation. The 2011 Roadmap is primarily concerned with medium to large-scale research infrastructure. However, any landmark infrastructure (typically involving an investment in excess of $100 million over five years from the Australian Government) requirements identified in this process will be noted. NRIC has also developed a ‘Process to identify and prioritise Australian Government landmark research infrastructure investments’ which is currently under consideration by the government as part of broader deliberations relating to research infrastructure. NRIC will have strategic oversight of the development of the 2011 Roadmap as part of its overall policy view of research infrastructure

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Automated annotation of landmark images using community contributed datasets and web resources

A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. "A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris". This exploits visual and textual web mining in combination with content-based image analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching oers 93.6% classication accuracy; the selection of appropriate tags for use in annotation performs well (F1M of 0.59), and it subsequently automatically identies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases

Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost

Robotic and animal mapping systems share many challenges and characteristics: they must function in a wide variety of environmental conditions, enable the robot or animal to navigate effectively to find food or shelter, and be computationally tractable from both a speed and storage perspective. With regards to map storage, the mammalian brain appears to take a diametrically opposed approach to all current robotic mapping systems. Where robotic mapping systems attempt to solve the data association problem to minimise representational aliasing, neurons in the brain intentionally break data association by encoding large (potentially unlimited) numbers of places with a single neuron. In this paper, we propose a novel method based on supervised learning techniques that seeks out regularly repeating visual patterns in the environment with mutually complementary co-prime frequencies, and an encoding scheme that enables storage requirements to grow sub-linearly with the size of the environment being mapped. To improve robustness in challenging real-world environments while maintaining storage growth sub-linearity, we incorporate both multi-exemplar learning and data augmentation techniques. Using large benchmark robotic mapping datasets, we demonstrate the combined system achieving high-performance place recognition with sub-linear storage requirements, and characterize the performance-storage growth trade-off curve. The work serves as the first robotic mapping system with sub-linear storage scaling properties, as well as the first large-scale demonstration in real-world environments of one of the proposed memory benefits of these neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and Automation Letter

LifeLogging: personal big data

We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self