Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metrictopological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability.

Persistent navigation and mapping using a biologically inspired SLAM system

The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment that is assumed to be in constant flux. We investigate the persistent navigation and mapping problem in the context of an autonomous robot that performs mock deliveries in a working office environment over a two-week period. The solution was based on the biologically inspired visual SLAM system, RatSLAM. RatSLAM performed SLAM continuously while interacting with global and local navigation systems, and a task selection module that selected between exploration, delivery, and recharging modes. The robot performed 1,143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), traveled a total distance of more than 40 km over 37 hours of active operation, and recharged autonomously a total of 23 times.

Hybrid robot control and SLAM for persistent navigation and mapping

For a mobile robot to operate autonomously in real-world environments, it must have an effective control system and a navigation system capable of providing robust localization, path planning and path execution. In this paper we describe the work investigating synergies between mapping and control systems. We have integrated development of a control system for navigating mobile robots and a robot SLAM system. The control system is hybrid in nature and tightly coupled with the SLAM system; it uses a combination of high and low level deliberative and reactive control processes to perform obstacle avoidance, exploration, global navigation and recharging, and draws upon the map learning and localization capabilities of the SLAM system. The effectiveness of this hybrid, multi-level approach was evaluated in the context of a delivery robot scenario. Over a period of two weeks the robot performed 1143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), travelled a total distance of more than 40km, and recharged autonomously a total of 23 times. In this paper we describe the combined control and SLAM system and discuss insights gained from its successful application in a real-world context.

A two week persistent navigation and mapping experiment using RatSLAM : insights and current developments

One of the major challenges in achieving long term robot autonomy is the need for a SLAM algorithm that can perform SLAM over the operational lifetime of the robot, preferably without human intervention or supervision. In this paper we present insights gained from a two week long persistent SLAM experiment, in which a Pioneer robot performed mock deliveries in a busy office environment. We used the biologically inspired visual SLAM system, RatSLAM, combined with a hybrid control architecture that selected between exploring the environment, performing deliveries, and recharging. The robot performed more than a thousand successful deliveries with only one failure (from which it recovered), travelled more than 40 km over 37 hours of active operation, and recharged autonomously 23 times. We discuss several issues arising from the success (and limitations) of this experiment and two subsequent avenues of work.

Towards persistent localization and mapping with a continuous appearance-based topology

Appearance-based localization can provide loop closure detection at vast scales regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale not only with the size of the environment but also with the operation time of the platform. Additionally, repeated visits to locations will develop multiple competing representations, which will reduce recall performance over time. These properties impose severe restrictions on long-term autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. In this paper we present a graphical extension to CAT-SLAM, a particle filter-based algorithm for appearance-based localization and mapping, to provide constant computation and memory requirements over time and minimal degradation of recall performance during repeated visits to locations. We demonstrate loop closure detection in a large urban environment with capped computation time and memory requirements and performance exceeding previous appearance-based methods by a factor of 2. We discuss the limitations of the algorithm with respect to environment size, appearance change over time and applications in topological planning and navigation for long-term robot operation.

Towards persistent indoor appearance-based localization, mapping and navigation using CAT-Graph

The challenge of persistent appearance-based navigation and mapping is to develop an autonomous robotic vision system that can simultaneously localize, map and navigate over the lifetime of the robot. However, the computation time and memory requirements of current appearance-based methods typically scale not only with the size of the environment but also with the operation time of the platform; also, repeated revisits to locations will develop multiple competing representations which reduce recall performance. In this paper we present a solution to the persistent localization, mapping and global path planning problem in the context of a delivery robot in an office environment over a one-week period. Using a graphical appearance-based SLAM algorithm, CAT-Graph, we demonstrate constant time and memory loop closure detection with minimal degradation during repeated revisits to locations, along with topological path planning that improves over time without using a global metric representation. We compare the localization performance of CAT-Graph to openFABMAP, an appearance-only SLAM algorithm, and the path planning performance to occupancy-grid based metric SLAM. We discuss the limitations of the algorithm with regard to environment change over time and illustrate how the topological graph representation can be coupled with local movement behaviors for persistent autonomous robot navigation.

Continuous appearance-based localisation and mapping

This thesis presents a novel approach to mobile robot navigation using visual information towards the goal of long-term autonomy. A novel concept of a continuous appearance-based trajectory is proposed in order to solve the limitations of previous robot navigation systems, and two new algorithms for mobile robots, CAT-SLAM and CAT-Graph, are presented and evaluated. These algorithms yield performance exceeding state-of-the-art methods on public benchmark datasets and large-scale real-world environments, and will help enable widespread use of mobile robots in everyday applications.

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.

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.

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.

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.