Odometry-driven inference to link multiple exemplars of a location

A major challenge for robot localization and mapping systems is maintaining reliable operation in a changing environment. Vision-based systems in particular are susceptible to changes in illumination and weather, and the same location at another time of day may appear radically different to a system using a feature-based visual localization system. One approach for mapping changing environments is to create and maintain maps that contain multiple representations of each physical location in a topological framework or manifold. However, this requires the system to be able to correctly link two or more appearance representations to the same spatial location, even though the representations may appear quite dissimilar. This paper proposes a method of linking visual representations from the same location without requiring a visual match, thereby allowing vision-based localization systems to create multiple appearance representations of physical locations. The most likely position on the robot path is determined using particle filter methods based on dead reckoning data and recent visual loop closures. In order to avoid erroneous loop closures, the odometry-based inferences are only accepted when the inferred path's end point is confirmed as correct by the visual matching system. Algorithm performance is demonstrated using an indoor robot dataset and a large outdoor camera dataset.

Autonomous movement-driven place recognition calibration for generic multi-sensor robot platforms

In this paper we present a method for autonomously tuning the threshold between learning and recognizing a place in the world, based on both how the rodent brain is thought to process and calibrate multisensory data and the pivoting movement behaviour that rodents perform in doing so. The approach makes no assumptions about the number and type of sensors, the robot platform, or the environment, relying only on the ability of a robot to perform two revolutions on the spot. In addition, it self-assesses the quality of the tuning process in order to identify situations in which tuning may have failed. We demonstrate the autonomous movement-driven threshold tuning on a Pioneer 3DX robot in eight locations spread over an office environment and a building car park, and then evaluate the mapping capability of the system on journeys through these environments. The system is able to pick a place recognition threshold that enables successful environment mapping in six of the eight locations while also autonomously flagging the tuning failure in the remaining two locations. We discuss how the method, in combination with parallel work on autonomous weighting of individual sensors, moves the parameter dependent RatSLAM system significantly closer to sensor, platform and environment agnostic operation.

Dealing with shadows : capturing intrinsic scene appearance for image-based outdoor localisation

In outdoor environments shadows are common. These typically strong visual features cause considerable change in the appearance of a place, and therefore confound vision-based localisation approaches. In this paper we describe how to convert a colour image of the scene to a greyscale invariant image where pixel values are a function of underlying material property not lighting. We summarise the theory of shadow invariant images and discuss the modelling and calibration issues which are important for non-ideal off-the-shelf colour cameras. We evaluate the technique with a commonly used robotic camera and an autonomous car operating in an outdoor environment, and show that it can outperform the use of ordinary greyscale images for the task of visual localisation.

Maternal exposure to heatwave and preterm birth in Brisbane, Australia

Objective To quantify the short-term effects of maternal exposure to heatwave on preterm birth. Design An ecological study. Setting: A population-based study in Brisbane, Australia. Population All pregnant women who had a spontaneous singleton live birth in Brisbane between November and March in 2000–2010 were studied. Methods Daily data on pregnancy outcomes, meteorological factors, and ambient air pollutants were obtained. The Cox proportional hazards regression model with time-dependent variables was used to examine the short-term impact of heatwave on preterm birth. A series of cut-off temperatures and durations were used to define heatwave. Multivariable analyses were also performed to adjust for socio-economic factors, demographic factors, meteorological factors, and ambient air pollutants. Main outcome measure Spontaneous preterm births. Results The adjusted hazard ratios (HRs) ranged from 1.13 (95% CI 1.03–1.24) to 2.00 (95% CI 1.37–2.91) by using different heatwave definitions, after controlling for demographic, socio-economic, and meteorological factors, and air pollutants. Conclusions Heatwave was significantly associated with preterm birth: the associations were robust to the definitions of heatwave. The threshold temperatures, instead of duration, could be more likely to influence the evaluation of birth-related heatwaves. The findings of this study may have significant public health implications as climate change progresses.

Intercontinental haptic teleoperation of a flying vehicle : a step towards real-time applications

This paper describes the theory and practice for a stable haptic teleoperation of a flying vehicle. It extends passivity-based control framework for haptic teleoperation of aerial vehicles in the longest intercontinental setting that presents great challenges. The practicality of the control architecture has been shown in maneuvering and obstacle-avoidance tasks over the internet with the presence of significant time-varying delays and packet losses. Experimental results are presented for teleoperation of a slave quadrotor in Australia from a master station in the Netherlands. The results show that the remote operator is able to safely maneuver the flying vehicle through a structure using haptic feedback of the state of the slave and the perceived obstacles.

Brain-inspired 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, 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.

Characterizing transport through a crowded environment with different obstacle sizes

Transport through crowded environments is often classified as anomalous, rather than classical, Fickian diffusion. Several studies have sought to describe such transport processes using either a continuous time random walk or fractional order differential equation. For both these models the transport is characterized by a parameter α, where α = 1 is associated with Fickian diffusion and α < 1 is associated with anomalous subdiffusion. Here, we simulate a single agent migrating through a crowded environment populated by impenetrable, immobile obstacles and estimate α from mean squared displacement data. We also simulate the transport of a population of such agents through a similar crowded environment and match averaged agent density profiles to the solution of a related fractional order differential equation to obtain an alternative estimate of α. We examine the relationship between our estimate of α and the properties of the obstacle field for both a single agent and a population of agents; we show that in both cases, α decreases as the obstacle density increases, and that the rate of decrease is greater for smaller obstacles. Our work suggests that it may be inappropriate to model transport through a crowded environment using widely reported approaches including power laws to describe the mean squared displacement and fractional order differential equations to represent the averaged agent density profiles.

Process model comprehension : the effects of cognitive abilities, learning style, and strategy

Process models are used to convey semantics about business operations that are to be supported by an information system. A wide variety of professionals is targeted to use such models, including people who have little modeling or domain expertise. We identify important user characteristics that influence the comprehension of process models. Through a free simulation experiment, we provide evidence that selected cognitive abilities, learning style, and learning strategy influence the development of process model comprehension. These insights draw attention to the importance of research that views process model comprehension as an emergent learning process rather than as an attribute of the models as objects. Based on our findings, we identify a set of organizational intervention strategies that can lead to more successful process modeling workshops.

Foot massage and physiological stress in people with dementia : a randomized controlled trial

Background There is increasing interest in using complementary and alternative treatments to manage behavioural and psychological symptoms of dementia such as agitation, aggression and depressed mood. Objective To compare the effect of foot massage (intervention) and quiet presence (control) on agitation and mood in people with dementia. Design A randomised controlled trial using a within-subjects, crossover design. Settings Five long-term care facilities in Brisbane, Australia. The primary outcome was the Cohen-Mansfield Agitation Inventory (CMAI) and the secondary outcome was the Observed Emotion Rating Scale (OERS). The screening and data collection research assistants, families, and care staff were blinded to participant allocation. Participants Participants of the study were 55 long-term care residents aged 74–103 years (mean age 86.5), with moderate to severe dementia and a history of agitated behaviour according to the Pittsburgh Agitation Scale. A computer-program randomised participants to 10-min foot massage (intervention) or quiet presence (control), every weekday for 3 weeks. Results A carry-over effect was identified in the data, and so the data was treated as a parallel groups RCT. The mean total CMAI increased in both groups (reflecting an increase in agitation) with this increase greater in the quiet presence group than the foot massage group (p=0.03). There was a trend towards a difference on OERS General Alertness, with a positive change in alertness for participants in the foot massage group (indicating reduced alertness) and a negative change for participants in the quiet presence group (indicating increased alertness) (F(1,51)=3.88, p=0.05, partial ή2=0.07). Conclusions The findings highlight the need for further research on the specific conditions under which massage might promote relaxation and improve mood for people with dementia. The unfamiliar research assistants and variations in usual activity may have contributed to the increase in agitation and this needs further research.

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.

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.

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.

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.

Towards condition-invariant, top-down visual place recognition

In this paper we present a novel place recognition algorithm inspired by recent discoveries in human visual neuroscience. The algorithm combines intolerant but fast low resolution whole image matching with highly tolerant, sub-image patch matching processes. The approach does not require prior training and works on single images (although we use a cohort normalization score to exploit temporal frame information), alleviating the need for either a velocity signal or image sequence, differentiating it from current state of the art methods. We demonstrate the algorithm on the challenging Alderley sunny day – rainy night dataset, which has only been previously solved by integrating over 320 frame long image sequences. The system is able to achieve 21.24% recall at 100% precision, matching drastically different day and night-time images of places while successfully rejecting match hypotheses between highly aliased images of different places. The results provide a new benchmark for single image, condition-invariant place recognition.

The effect of nitroxides on swarming motility and biofilms, multicellular behaviors in Pseudomonas aeruginosa

The ability of NO to induce biofilm dispersion has been well established. Here we investigated the effect of nitroxides (sterically hindered nitric oxide analogues) on biofilm formation and swarming motility in Pseudomonas aeruginosa. A transposon mutant unable to produce nitric oxide endogenously (nirS) was deficient in swarming motility relative to wild type and the complemented strain. Moreover, expression of the nirS gene was up-regulated by 9.65-fold in wild type swarming cells when compared to planktonic cells. Wild type swarming levels were substantially restored upon exogenous addition of nitroxide containing compounds, consistent with the hypothesis that NO is necessary for swarming motility. Here, we showed that nitroxides not only mimicked the dispersal activity of NO, but also prevented biofilms from forming in flow cell chambers. In addition, a nirS transposon mutant was deficient in biofilm formation relative to wild type and the complemented strain, thus implicating NO in the formation of biofilms. Intriguingly despite its stand alone action in inhibiting biofilm formation and promoting dispersal, a nitroxide partially restored the ability of a nirS mutant to form biofilms.