Mapping of indoor environments by robots using low-cost vision sensors

For robots to operate in human environments they must be able to make their own maps because it is unrealistic to expect a user to enter a map into the robot’s memory; existing floorplans are often incorrect; and human environments tend to change. Traditionally robots have used sonar, infra-red or laser range finders to perform the mapping task. Digital cameras have become very cheap in recent years and they have opened up new possibilities as a sensor for robot perception. Any robot that must interact with humans can reasonably be expected to have a camera for tasks such as face recognition, so it makes sense to also use the camera for navigation. Cameras have advantages over other sensors such as colour information (not available with any other sensor), better immunity to noise (compared to sonar), and not being restricted to operating in a plane (like laser range finders). However, there are disadvantages too, with the principal one being the effect of perspective. This research investigated ways to use a single colour camera as a range sensor to guide an autonomous robot and allow it to build a map of its environment, a process referred to as Simultaneous Localization and Mapping (SLAM). An experimental system was built using a robot controlled via a wireless network connection. Using the on-board camera as the only sensor, the robot successfully explored and mapped indoor office environments. The quality of the resulting maps is comparable to those that have been reported in the literature for sonar or infra-red sensors. Although the maps are not as accurate as ones created with a laser range finder, the solution using a camera is significantly cheaper and is more appropriate for toys and early domestic robots.

K-tree : large scale document clustering

We introduce K-tree in an information retrieval context. It is an efficient approximation of the k-means clustering algorithm. Unlike k-means it forms a hierarchy of clusters. It has been extended to address issues with sparse representations. We compare performance and quality to CLUTO using document collections. The K-tree has a low time complexity that is suitable for large document collections. This tree structure allows for efficient disk based implementations where space requirements exceed that of main memory.

A new educational technology for media and communication studies : mapping media in Australia and Sweden

This chapter reports on Australian and Swedish experiences in the iterative design, development, and ongoing use of interactive educational systems we call ‘Media Maps.’ Like maps in general, Media Maps are usefully understood as complex cultural technologies; that is, they are not only physical objects, tools and artefacts, but also information creation and distribution technologies, the use and development of which are embedded in systems of knowledge and social meaning. Drawing upon Australian and Swedish experiences with one Media Map technology, this paper illustrates this three-layered approach to the development of media mapping. It shows how media mapping is being used to create authentic learning experiences for students preparing for work in the rapidly evolving media and communication industries. We also contextualise media mapping as a response to various challenges for curriculum and learning design in Media and Communication Studies that arise from shifts in tertiary education policy in a global knowledge economy.

How is a fruit tree like you? Using artistic metaphors to explore and develop emotional competence in children

Counselling children often requires the use of supplementary strategies in order to interest and engage the child in the therapeutic process. One such strategy is the Metaphorical Fruit Tree (MFT); an art metaphor suited to exploring and developing self-concept. Quantitative and qualitative data was used to explore the relationships between children’s ability to use metaphor, age, gender, and level of emotional competence (N = 58). Quantitative and qualitative analyses revealed a significant negative relationship between self-reported emotional competence and ability to use the MFT. It is proposed that children rely on different processes to understand self and as children’s ability to cognitively report on their emotional capabilities via the Emotional Competence Questionnaire (ECQ) increases, their ability to report creatively on those capabilities via the MFT is undermined. It is suggested that the MFT may be used, via creative processes and as an alternative to cognitive processes, to increase understanding and awareness of intrapersonal and interpersonal concepts of self in the child during counselling.

Mapping the regulatory environment : implications for construction firms

As regulators, governments are often criticised for over‐regulating industries. This research project seeks to examine the regulation affecting the construction industry in a federal system of government. It uses a case study of the Australian system of government to focus on the question of the implications of regulation in the construction industry. Having established the extent of the regulatory environment, the research project considers the costs associated with this environment. Consequently, ways in which the regulatory burden on industry can be reduced are evaluated. The Construction Industry Business Environment project is working with industry and government agencies to improve regulatory harmonisation in Australia, and thereby reduce the regulatory burden on industry. It is found that while taxation and compliance costs are not likely to be reduced in the short term, costs arising from having to adapt to variation between regulatory regimes in a federal system of government, seem the most promising way of reducing regulatory costs. Identifying and reducing adaptive costs across jurisdictional are argued to present a novel approach to regulatory reform.

Curriculum mapping of the Spatial Science Program, QUT : what was the cartography journey?

There are currently a number of issues of great importance affecting universities and the way in which their programs are now offered. Many issues are largely being driven top-down and impact both at a university-wide and at an individual discipline level. This paper provides a brief history of cartography and digital mapping education at the Queensland University of Technology (QUT). It also provides an overview of what is curriculum mapping and presents some interesting findings from the program review process. Further, this review process has triggered discussion and action for the review, mapping and embedding of graduate attributes within the spatial science major program. Some form of practical based learning is expected in vocationally oriented degrees that lead to professional accreditation and are generally regarded as a good learning exposure. With the restructure of academic programs across the Faculty of Built Environment and Engineering in 2006, spatial science and surveying students now undertake a formal work integrated learning unit. There is little doubt that students acquire the skills of their discipline (mapping science, spatial) by being immersed in the industry culture- learning how to process information and solve real-world problems within context. The broad theme of where geo-spatial mapping skills are embedded in this broad-based tertiary education course are examined with some focused discussion on the learning objectives, outcomes and examples of some student learning experiences

Mapping the Australian political blogosphere

Network crawling and visualisation tools and other datamining systems are now advanced enough to provide significant new impulses to the study of cultural activity on the Web. A growing range of studies focus on communicative processes in the blogosphere – including for example Adamic & Glance’s 2005 map of political allegiances during the 2004 U.S. presidential election and Kelly & Etling’s 2008 study of blogging practices in Iran. There remain a number of significant shortcomings in the application of such tools and methodologies to the study of blogging; these relate both to how the content of blogs is analysed, and to how the network maps resulting from such studies are understood. Our project highlights and addresses such shortcomings.

Document clustering with K-tree

This paper describes the approach taken to the XML Mining track at INEX 2008 by a group at the Queensland University of Technology. We introduce the K-tree clustering algorithm in an Information Retrieval context by adapting it for document clustering. Many large scale problems exist in document clustering. K-tree scales well with large inputs due to its low complexity. It offers promising results both in terms of efficiency and quality. Document classification was completed using Support Vector Machines.

Green Marketing Communities and blogs : mapping consumer’s attitudes for future sustainable marketing

The last three decades have seen consumers’ environmental consciousness grow as the environment has moved to a mainstream issue. Results from our study of green marketing blog site comments in the first half of 2009 finds thirteen prominent concepts: carbon, consumers, global and energy were the largest themes, while crisis, power, people, water, fuel, product, work, time, water, organic, content and interest were the others. However sub issues were also identified, as the driving factor of this information is coming from consumer led social networks. While marketers hold some power, consumers are the real key factor to possess influence for change. They want to drive change and importantly, they have the power. Power to the people.

Mapping personal trip OD from probe data

The paper analyses the expected value of OD volumes from probe with fixed error, error that is proportional to zone size and inversely proportional to zone size. To add realism to the analysis, real trip ODs in the Tokyo Metropolitan Region are synthesised. The results show that for small zone coding with average radius of 1.1km, and fixed measurement error of 100m, an accuracy of 70% can be expected. The equivalent accuracy for medium zone coding with average radius of 5km would translate into a fixed error of approximately 300m. As expected small zone coding is more sensitive than medium zone coding as the chances of the probe error envelope falling into adjacent zones are higher. For the same error radii, error proportional to zone size would deliver higher level of accuracy. As over half (54.8%) of the trip ends start or end at zone with equivalent radius of ≤ 1.2 km and only 13% of trips ends occurred at zones with equivalent radius ≥2.5km, measurement error that is proportional to zone size such as mobile phone would deliver higher level of accuracy. The synthesis of real OD with different probe error characteristics have shown that expected value of >85% is difficult to achieve for small zone coding with average radius of 1.1km. For most transport applications, OD matrix at medium zone coding is sufficient for transport management. From this study it can be drawn that GPS with error range between 2 and 5m, and at medium zone coding (average radius of 5km) would provide OD estimates greater than 90% of the expected value. However, for a typical mobile phone operating error range at medium zone coding the expected value would be lower than 85%. This paper assumes transmission of one origin and one destination positions from the probe. However, if multiple positions within the origin and destination zones are transmitted, map matching to transport network could be performed and it would greatly improve the accuracy of the probe data.

Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction

The application of object-based approaches to the problem of extracting vegetation information from images requires accurate delineation of individual tree crowns. This paper presents an automated method for individual tree crown detection and delineation by applying a simplified PCNN model in spectral feature space followed by post-processing using morphological reconstruction. The algorithm was tested on high resolution multi-spectral aerial images and the results are compared with two existing image segmentation algorithms. The results demonstrate that our algorithm outperforms the other two solutions with the average accuracy of 81.8%.