Exploring expedition research methodology : a personal reflection

Research into the human dynamics of expeditions is a potentially rewarding and fruitful area of study. However, the complex nature of expedition work presents the researcher with numerous challenges. This paper presents a personal reflection on the challenges linked to determining appropriate methodological processes for a study into expedition teamwork. Previous expedition research is outlined and reviewed for appropriateness. Some alternative methodological theories are described and limitations highlighted. Lastly the actual data gathering and analysis processes are detailed. The aim being to show that what happened in the field inevitably dictated how methodological processes were adapted. Essentially the paper describes a personal journey into research. A journey that sparked numerous personal insights in the science of human dynamics and expeditions and one that I hope will add to the development of expedition research in general.

One robot, eight hours, and twenty four thousand people

This paper presents an account of an autonomous mobile robot deployment in a densely crowded public event with thousands of people from different age groups attending. The robot operated for eight hours on an open floor surrounded by tables, chairs and massive touchscreen displays. Due to the large number of people who were in close vicinity of the robot, different safety measures were implemented including the use of no-go zones which prevent the robot from blocking emergency exits or moving too close to the display screens. The paper presents the lessons learnt and experiences obtained from this experiment, and provides a discussion about the state of mobile service robots in such crowded environments.

Kaleidoscope : shaping a better future through a stakeholder approach to widespread organisational change

The main focus of ‘Kaleidoscope: Reframing evaluation through a stakeholder approach to sustainable, cultural change in Higher Education’ is to develop a set of principles to guide user-led engagement in widespread organisational change and maximise its impact. The word kaleidoscope represents the unique lens through which each institution will need to view their cultural specificity and local context through an extensive process of collaboration and engagement, followed by communication and dissemination. Kaleidoscope has particular relevance when new approaches to learning and teaching evaluation are introduced by tertiary institutions. Building on the Reframe Project, which involved three years of user-led consultation and was designed to meet stakeholders’ needs, QUT successfully introduced a new evaluation framework in 2013 across the university. Reframe was evidence based, involved scholarly reflection and was founded on a strong theoretical framework. The evolution of the evaluation framework included analysis of scholarly literature and environmental scans across the higher education sector (Alderman, et al., 2012), researched development of conceptual theory (Alderman, et al., in press 2013), incorporated the stakeholder voice and framed within project management principles (Alderman & Melanie, 2012). Kaleidoscope’s objectives are for QUT to develop its research-based stakeholder approach to distil the successful experience exhibited in the Reframe Project into a transferable set of guidelines for use by other tertiary institutions across the sectors. These guidelines will assist others to design, develop, and deploy, their own culturally specific widespread organisational change informed by stakeholder engagement and organisational buy-in. It is intended that these guidelines will promote, support and enable other tertiary institutions to embark on their own projects and maximise the impact. In correlation with a our conference paper, this round table presents the Draft Guidelines and Framework ready for external peer review by evaluation practitioners, as part of Kaleidoscope’s dissemination (Hinton & Gannaway, 2011) applying illuminative evaluation theory (Parlett & Hamilton, 1976), through conference workshops and linked round table discussions (Shapiro, et al., 1983; Jacobs, 2000).

An examination of the legislative framework governing commuter safety in the Queensland Mining Industry

Commuting in the mining industry -Background -The problem -Journey management -The structure of the legislative framework Legislation and Regulation -Workplace safety in Queensland mining -Risk management -Mining legislation and journey management -Commuting and employee responsibilities -Queensland Workers’ Compensation Scheme Industry standards -Industry standards and journey management Regulated and organisational policy documents -Policy documents and journey management Observations & Conclusions

Patients perceptions of emergency department services : better specialised staff, convenient and available services

Many factors are identified as contributing to the high demand for emergency department (ED) care. Similarly, there have been many initiatives taken to minimise the impact that is placed on EDs. Many of these, however, do not consider the patient's opinions and motivations. The aim of this cross-sectional study was to understand patients’ perspectives and reasons behind their decision to present to EDs. 911 surveys were collected from patients presenting to eight QLD EDs in 2011. Based on the Principal Component Analysis technique, a six-item scale entitled "Best services at emergency departments" was extracted (α = 0.729) measuring patients' opinions and perspectives. Further, the independent t-tests were conducted between various groups of ED users. The results suggest that multiple users more likely viewed EDs as the best place for their conditions than the first-time users (Median 10.73 v 11.56, p<0.001). Moreover, patients who made the decision to present by themselves had a more favourable perception of the ED services than those for whom the decision was made or others were involved (Median 11.38 v 10.80, p=0.003). Method of arrival did not affect the respondents’ perception of ED (11.13 v 11.00, p=0.65). The results of this research indicate that patients’ perception of ED as the best and most appropriate place for attention to their medical conditions plays an important role in their decision to present and keep returning to ED. Understanding patients’ reasons and decisions enhances the success of planning and implementing alternative services to manage the demand for ED services.

Towards persistent visual navigation using SMART

In this paper, we present SMART (Sequence Matching Across Route Traversals): a vision- based place recognition system that uses whole image matching techniques and odometry information to improve the precision-recall performance, latency and general applicability of the SeqSLAM algorithm. We evaluate the system’s performance on challenging day and night journeys over several kilometres at widely varying vehicle velocities from 0 to 60 km/h, compare performance to the current state-of- the-art SeqSLAM algorithm, and provide parameter studies that evaluate the effectiveness of each system component. Using 30-metre sequences, SMART achieves place recognition performance of 81% recall at 100% precision, outperforming SeqSLAM, and is robust to significant degradations in odometry.

A novel method for analysing lighting variance

Robust descriptor matching across varying lighting conditions is important for vision-based robotics. We present a novel strategy for quantifying the lighting variance of descriptors. The strategy works by utilising recovered low dimensional mappings from Isomap and our measure of the lighting variance of each of these mappings. The resultant metric allows different descriptors to be compared given a dataset and a set of keypoints. We demonstrate that the SIFT descriptor typically has lower lighting variance than other descriptors, although the result depends on semantic class and lighting conditions.

Advantages of exploiting projection structure for segmenting dense 3D point clouds

Timely and comprehensive scene segmentation is often a critical step for many high level mobile robotic tasks. This paper examines a projected area based neighbourhood lookup approach with the motivation towards faster unsupervised segmentation of dense 3D point clouds. The proposed algorithm exploits the projection geometry of a depth camera to find nearest neighbours which is time independent of the input data size. Points near depth discontinuations are also detected to reinforce object boundaries in the clustering process. The search method presented is evaluated using both indoor and outdoor dense depth images and demonstrates significant improvements in speed and precision compared to the commonly used Fast library for approximate nearest neighbour (FLANN) [Muja and Lowe, 2009].

Low cost localisation for agricultural robotics

This paper presents a pose estimation approach that is resilient to typical sensor failure and suitable for low cost agricultural robots. Guiding large agricultural machinery with highly accurate GPS/INS systems has become standard practice, however these systems are inappropriate for smaller, lower-cost robots. Our positioning system estimates pose by fusing data from a low-cost global positioning sensor, low-cost inertial sensors and a new technique for vision-based row tracking. The results first demonstrate that our positioning system will accurately guide a robot to perform a coverage task across a 6 hectare field. The results then demonstrate that our vision-based row tracking algorithm improves the performance of the positioning system despite long periods of precision correction signal dropout and intermittent dropouts of the entire GPS sensor.

μAV - Design and implementation of an open source micro quadrotor

This paper presents the design of μAV, a palm size open source micro quadrotor constructed on a single Printed Circuit Board. The aim of the micro quadrotor is to provide a lightweight (approximately 86g) and cheap robotic research platform that can be used for a range of robotic applications. One possible application could be a cheap test bed for robotic swarm research. The goal of this paper is to give an overview of the design and capabilities of the micro quadrotor. The micro quadrotor is complete with a 9 Degree of Freedom Inertial Measurement Unit, a Gumstix Overo® Computer-On-Module which can run the widely used Robot Operating System (ROS) for use with other research algorithms.

Training-free probability models for whole-image based place recognition

Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

Improved line tracker using IMU and vision for visual servoing

This paper introduces an improved line tracker using IMU and vision data for visual servoing tasks. We utilize an Image Jacobian which describes motion of a line feature to corresponding camera movements. These camera motions are estimated using an IMU. We demonstrate impacts of the proposed method in challenging environments: maximum angular rate ~160 0/s, acceleration ~6m /s2 and in cluttered outdoor scenes. Simulation and quantitative tracking performance comparison with the Visual Servoing Platform (ViSP) are also presented.

Towards bio-inspired place recognition over multiple spatial scales

This paper presents a new multi-scale place recognition system inspired by the recent discovery of overlapping, multi-scale spatial maps stored in the rodent brain. By training a set of Support Vector Machines to recognize places at varying levels of spatial specificity, we are able to validate spatially specific place recognition hypotheses against broader place recognition hypotheses without sacrificing localization accuracy. We evaluate the system in a range of experiments using cameras mounted on a motorbike and a human in two different environments. At 100% precision, the multiscale approach results in a 56% average improvement in recall rate across both datasets. We analyse the results and then discuss future work that may lead to improvements in both robotic mapping and our understanding of sensory processing and encoding in the mammalian brain.

An analysis of regression models for predicting the speed of a wave glider autonomous surface vehicle

An important aspect of robotic path planning for is ensuring that the vehicle is in the best location to collect the data necessary for the problem at hand. Given that features of interest are dynamic and move with oceanic currents, vehicle speed is an important factor in any planning exercises to ensure vehicles are at the right place at the right time. Here, we examine different Gaussian process models to find a suitable predictive kinematic model that enable the speed of an underactuated, autonomous surface vehicle to be accurately predicted given a set of input environmental parameters.

Using image quality metrics to detect smoke-affected laser data

Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.