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.

Capping computation time and storage requirements for appearance-based localization with CAT-SLAM

Appearance-based localization is increasingly used for loop closure detection in metric SLAM systems. Since it relies only upon the appearance-based similarity between images from two locations, it can perform loop closure regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale linearly not only with the size of the environment but also with the operation time of the platform. These properties impose severe restrictions on longterm autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. We present a set of improvements to the appearance-based SLAM algorithm CAT-SLAM to constrain computation scaling and memory usage with minimal degradation in performance over time. The appearance-based comparison stage is accelerated by exploiting properties of the particle observation update, and nodes in the continuous trajectory map are removed according to minimal information loss criteria. We demonstrate constant time and space loop closure detection in a large urban environment with recall performance exceeding FAB-MAP by a factor of 3 at 100% precision, and investigate the minimum computational and memory requirements for maintaining mapping performance.

Integrated vision-based attitude estimation

For many years, computer vision has lured researchers with promises of a low-cost, passive, lightweight and information-rich sensor suitable for navigation purposes. The prime difficulty in vision-based navigation is that the navigation solution will continually drift with time unless external information is available, whether it be cues from the appearance of the scene, a map of features (whether built online or known a priori), or from an externally-referenced sensor. It is not merely position that is of interest in the navigation problem. Attitude (i.e. the angular orientation of a body with respect to a reference frame) is integral to a visionbased navigation solution and is often of interest in its own right (e.g. flight control). This thesis examines vision-based attitude estimation in an aerospace environment, and two methods are proposed for constraining drift in the attitude solution; one through a novel integration of optical flow and the detection of the sky horizon, and the other through a loosely-coupled integration of Visual Odometry and GPS position measurements. In the first method, roll angle, pitch angle and the three aircraft body rates are recovered though a novel method of tracking the horizon over time and integrating the horizonderived attitude information with optical flow. An image processing front-end is used to select several candidate lines in a image that may or may not correspond to the true horizon, and the optical flow is calculated for each candidate line. Using an Extended Kalman Filter (EKF), the previously estimated aircraft state is propagated using a motion model and a candidate horizon line is associated using a statistical test based on the optical flow measurements and location of the horizon in the image. Once associated, the selected horizon line, along with the associated optical flow, is used as a measurement to the EKF. To evaluate the accuracy of the algorithm, two flights were conducted, one using a highly dynamic Uninhabited Airborne Vehicle (UAV) in clear flight conditions and the other in a human-piloted Cessna 172 in conditions where the horizon was partially obscured by terrain, haze and smoke. The UAV flight resulted in pitch and roll error standard deviations of 0.42° and 0.71° respectively when compared with a truth attitude source. The Cessna 172 flight resulted in pitch and roll error standard deviations of 1.79° and 1.75° respectively. In the second method for estimating attitude, a novel integrated GPS/Visual Odometry (GPS/VO) navigation filter is proposed, using a structure similar to a classic looselycoupled GPS/INS error-state navigation filter. Under such an arrangement, the error dynamics of the system are derived and a Kalman Filter is developed for estimating the errors in position and attitude. Through similar analysis to the GPS/INS problem, it is shown that the proposed filter is capable of recovering the complete attitude (i.e. pitch, roll and yaw) of the platform when subjected to acceleration not parallel to velocity for both the monocular and stereo variants of the filter. Furthermore, it is shown that under general straight line motion (e.g. constant velocity), only the component of attitude in the direction of motion is unobservable. Numerical simulations are performed to demonstrate the observability properties of the GPS/VO filter in both the monocular and stereo camera configurations. Furthermore, the proposed filter is tested on imagery collected using a Cessna 172 to demonstrate the observability properties on real-world data. The proposed GPS/VO filter does not require additional restrictions or assumptions such as platform-specific dynamics, map-matching, feature-tracking, visual loop-closing, gravity vector or additional sensors such as an IMU or magnetic compass. Since no platformspecific dynamics are required, the proposed filter is not limited to the aerospace domain and has the potential to be deployed in other platforms such as ground robots or mobile phones.

Towards a comprehensive and integrated knowledge-based urban development model : status quo and directions

Incorporating knowledge based urban development (KBUD) strategies in the urban planning and development process is a challenging and complex task due to the fragmented and incoherent nature of the existing KBUD models. This paper scrutinizes and compares these KBUD models with an aim of identifying key and common features that help in developing a new comprehensive and integrated KBUD model. The features and characteristics of the existing KBUD models are determined through a thorough literature review and the analysis reveals that while these models are invaluable and useful in some cases, lack of a comprehensive perspective and absence of full integration of all necessary development domains render them incomplete as a generic model. The proposed KBUD model considers all central elements of urban development and sets an effective platform for planners and developers to achieve more holistic development outcomes. The proposed model, when developed further, has a high potential to support researchers, practitioners and particularly city and state administrations that are aiming to a knowledge-based development.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Developing a simulator application to test the billing platform in telecom industry

Billing Mediation Platform (BMP) in telecommunication industry is used to process real-time streams of Call Detail Records (CDRs) which can be a massive number a day. The generated records by BMP can be deployed for billing purposes, fraud detection, spam filtering, traffic analysis, and churn forecast. Several of these applications are distinguished by real-time processing requiring low-latency analysis of CDRs. Testing of such a platform carries diverse aspects like stress testing of analytics for scalability and what-if scenarios which require generating of CDRs with realistic volumetric and appropriate properties. The approach of this project is to build user friendly and flexible application which assists the development department to test their billing solution occasionally. These generators projects have been around for a while the only difference are the potions they cover and the purpose they will be used for. This paper proposes to use a simulator application to test the BMPs with simulating CDRs. The Simulated CDRs are modifiable based on the user requirements and represent real world data.

Plant-made therapeutics: An emerging platform in South Africa

The field of plant-made therapeutics in South Africa is well established in the form of exploitation of the country's considerable natural plant diversity, both in the use of native plants in traditional herbal medicines over many centuries, and in the more modern extraction of pharmacologically-active compounds from plants, including those known to traditional healers. In recent years, this has been added to by the use of plants for the stable or transient expression of pharmaceutically-important compounds, largely protein-based biologics and vaccines. South Africa has a well-developed plant biotechnology community, as well as a comprehensive legislative framework for the regulation of the exploitation of local botanic resources, and of genetically-modified organisms. The review explores the investigation of both conventional and recombinant plants for pharmaceutical use in South Africa, as well as describing the relevant legislative and regulatory frameworks. Potential opportunities for national projects, as well as factors limiting biopharming in South Africa are discussed. © 2011.

First year industrial design studio-based teaching pedagogies and students’ engagement

Student engagement tends to be viewed as a reflection of learning processes, and in the context of first year university studies, it is a crucial means of an educational process that establishes the foundations for successful later year studies (Krausse and Coates, 2008). In the context of first year design studio teaching in higher education, fostering students’ positive engagement poses challenges to design educators as current trends set these design studios to be large size classes that makes difficult to manage and follow up students’ individual learning experiences. At QUT’s first year industrial design studio classes we engage in a variety of teaching pedagogies from which we identify two of them as instrumental vehicles to foster positive student engagement. Concept bombs and the field trip experience provide such platform as shown in student responses through a learning experience survey.

Computational experiments involving population size for FPGA-based implementation of a GA for the TSP

The feasibility of using an in-hardware implementation of a genetic algorithm (GA) to solve the computationally expensive travelling salesman problem (TSP) is explored, especially in regard to hardware resource requirements for problem and population sizes. We investigate via numerical experiments whether a small population size might prove sufficient to obtain reasonable quality solutions for the TSP, thereby permitting relatively resource efficient hardware implementation on field programmable gate arrays (FPGAs). Software experiments on two TSP benchmarks involving 48 and 532 cities were used to explore the extent to which population size can be reduced without compromising solution quality, and results show that a GA allowed to run for a large number of generations with a smaller population size can yield solutions of comparable quality to those obtained using a larger population. This finding is then used to investigate feasible problem sizes on a targeted Virtex-7 vx485T-2 FPGA platform via exploration of hardware resource requirements for memory and data flow operations.

A New DER coordination in LV network based on the concept of distributed control

Given the paradigm of smart grid as the promising backbone for future network, this paper uses this paradigm to propose a new coordination approach for LV network based on distributed control algorithm. This approach divides the LV network into hierarchical communities where each community is controlled by a control agent. Different level of communication has been proposed for this structure to control the network in different operation modes.

Polycaprolactone-based scaffold plus recombinant human bone morphogenic protein rhBMP-2) in a sheep thoracic spine fusion model

Adolescent idiopathic scoliosis is a complex three dimensional deformity affecting 2-3% of the general population. The resulting spinal deformity consists of coronal curvature, hypokyphosis of the thoracic spine and vertebral rotation in the axial plane with posterior elements turned into the curve concavity. The potential for curve progression is heightened during the adolescent growth spurt. Success of scoliosis deformity correction depends on solid bony fusion between adjacent vertebrae after the intervertebral (IV) discs have been surgically cleared and the disc spaces filled with graft material. Recently a bioactive and resorbable scaffold fabricated from medical grade polycaprolactone has been developed for bone regeneration at load bearing sites. Combined with rhBMP-2, this has been shown to be successful in acting as a bone graft substitute in a porcine lumbar interbody fusion model when compared to autologous bone graft alone. The study aimed to establish a large animal thoracic spine interbody fusion model, develop spine biodegradable scaffolds (PCL) in combination with biologics (rhBMP-2) and to establish a platform for research into spine tissue engineering constructs. Preliminary results demonstrate higher grades of radiologically evident bony fusion across all levels when comparing fusion scores between the 3 and 6 month postop groups at the PCL CaP coated scaffold level, which is observed to be a similar grade to autograft, while no fusion is seen at the scaffold only level. Results to date suggest that the combination of rhBMP-2 and scaffold engineering actively promotes bone formation, laying the basis of a viable tissue engineered constructs.

Biomechanical performance of polycaprolactone (PCL)-based scaffold with rhBMP-2 in a sheep thoracic spine fusion model

Adolescent idiopathic scoliosis is a complex three dimensional deformity affecting 2-3% of the general population. Resulting spine deformities include progressive coronal curvature, hypokyphosis, or frank lordosis in the thoracic spine and vertebral rotation in the axial plane with posterior elements turned into the curve concavity. The potential for curve progression is heightened during the adolescent growth spurt. Success of scoliosis deformity correction depends on solid bony fusion between adjacent vertebrae after the intervertebral discs have been surgically cleared and the disc spaces filled with graft material. Problems with bone graft harvest site morbidity as well as limited bone availability have led to the search for bone graft substitutes. Recently, a bioactive and resorbable scaffold fabricated from medical grade polycaprolactone (PCL) has been developed for bone regeneration at load bearing sites. Combined with recombinant human bone morphogenic protein–2 (rhBMP-2), this has been shown to be successful in acting as a bone graft substitute in acting as a bone graft substitute in a porcine lumbar interbody fusion model when compared to autologous bone graft. This in vivo sheep study intends to evaluate the suitability of a custom designed medical grade PCL scaffold in combination with rhBMP-2 as a bone graft substitute in the setting of mini–thoracotomy surgery as a platform for ongoing research to benefit patients with adolescent idiopathic scoliosis.

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.

Design minds : an online design education platform for non-designers to enact cultural change

Education in the 21st century demands a model for understanding a new culture of learning in the face of rapid change, open access data and geographical diversity. Teachers no longer need to provide the latest information because students themselves are taking an active role in peer collectives to help create it. This paper examines, through an Australian case study entitled ‘Design Minds’, the development of an online design education platform as a key initiative to enact a government priority for state-wide cultural change through design-based curriculum. Utilising digital technology to create a supportive community, ‘Design Minds’ recognises that interdisciplinary learning fostered through engagement will empower future citizens to think, innovate, and discover. This paper details the participatory design process undertaken with multiple stakeholders to create the platform. It also outlines a proposed research agenda for future measurement of its value in creating a new learning culture, supporting regional and remote communities, and revitalising frontline services. It is anticipated this research will inform ongoing development of the online platform, and future design education and research programs in K-12 schools in Australia.

Towards brain-based sensor fusion for navigating robots

Current state of the art robot mapping and navigation systems produce impressive performance under a narrow range of robot platform, sensor and environmental conditions, in contrast to animals such as rats that produce “good enough” maps that enable them to function under an incredible range of situations. In this paper we present a rat-inspired featureless sensor-fusion system that assesses the usefulness of multiple sensor modalities based on their utility and coherence for place recognition during a navigation task, without knowledge as to the type of sensor. We demonstrate the system on a Pioneer robot in indoor and outdoor environments with abrupt lighting changes. Through dynamic weighting of the sensors, the system is able to perform correct place recognition and mapping where the static sensor weighting approach fails.