264 resultados para Outdoor


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Objectives: We aimed to identify current practice of sun protection and factors associated with effective use in four outdoor worker industries in Queensland, Australia. Methods: Workplaces in four industries with a high proportion of outdoor workers (building/construction, rural/farming, local government, and public sector industries) were identified using an online telephone directory, screened for eligibility, and invited to participant via mail (n=15, recruitment rate 37%). A convenience sample of workers were recruited within each workplace (n=162). Workplaces’ sun protective policies and procedures were identified using interviews and policy analysis with workplace representatives, and discussion groups and computer-assisted telephone interviews with workers. Personal characteristics and sun protection knowledge, attitudes and behaviors were collated and analysed. Results: Just over half the workplaces had an existing policy which referred to sun protection (58%), and most provided at least some personal protective equipment (PPE), but few scheduled work outside peak sun hours (43%) or provided skin checks (21%). Several worker and workplace characteristics were associated with greater sun protection behaviour among workers, including having received education on the use of PPE (p<0.001), being concerned about being in the sun (p=0.002); and working in a smaller workplace (p=0.035). Conclusions: Uptake of sun protection by outdoor workers is affected by a complex interplay of both workplace and personal factors, and there is a need for effective strategies targeting both the workplace environment and workers’ knowledge, attitudes and behaviors to decrease harmful sun exposure further.

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BACKGROUND: Outdoor workers are at high risk of harmful ultraviolet radiation exposure and are identified as an at risk group for the development of skin cancer. This systematic evidence based review provides an update to a previous review published in 2007 about interventions for the prevention of skin cancer in outdoor workers. RESULTS: This review includes interventions published between 2007-2012 and presents findings about sun protection behaviours and/or objective measures of skin cancer risk. Six papers met inclusion criteria and were included in the review. Large studies with extended follow-up times demonstrated the efficacy of educational and multi-component interventions to increase sun protection, with some higher use of personal protective equipment such as sunscreen. However, there is less evidence for the effectiveness of policy or specific intervention components. CONCLUSIONS: Further research aimed at improving overall attitudes towards sun protection in outdoor workers is needed to provide an overarching framework.

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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.

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This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke. An example of application is given with monocular SLAM estimating the pose of the UGV while smoke is present in the environment. It is shown that the proposed novel quality metric can be used to anticipate situations where the quality of the pose estimate will be significantly degraded due to the input image data. This leads to decisions of advantageously switching between data sources (e.g. using infrared images instead of visual images).

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This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke.

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This work aims to promote integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicles equipped with a camera and a 2D laser range finder. A method to check for inconsistencies between the data provided by these two heterogeneous sensors is proposed and discussed. First, uncertainties in the estimated transformation between the laser and camera frames are evaluated and propagated up to the projection of the laser points onto the image. Then, for each pair of laser scan-camera image acquired, the information at corners of the laser scan is compared with the content of the image, resulting in a likelihood of correspondence. The result of this process is then used to validate segments of the laser scan that are found to be consistent with the image, while inconsistent segments are rejected. Experimental results illustrate how this technique can improve the reliability of perception in challenging environmental conditions, such as in the presence of airborne dust.

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This paper presents an approach to autonomously monitor the behavior of a robot endowed with several navigation and locomotion modes, adapted to the terrain to traverse. The mode selection process is done in two steps: the best suited mode is firstly selected on the basis of initial information or a qualitative map built on-line by the robot. Then, the motions of the robot are monitored by various processes that update mode transition probabilities in a Markov system. The paper focuses on this latter selection process: the overall approach is depicted, and preliminary experimental results are presented

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Covertly tracking mobile targets, either animal or human, in previously unmapped outdoor natural environments using off-road robotic platforms requires both visual and acoustic stealth. Whilst the use of robots for stealthy surveillance is not new, the majority only consider navigation for visual covertness. However, most fielded robotic systems have a non-negligible acoustic footprint arising from the onboard sensors, motors, computers and cooling systems, and also from the wheels interacting with the terrain during motion. This time-varying acoustic signature can jeopardise any visual covertness and needs to be addressed in any stealthy navigation strategy. In previous work, we addressed the initial concepts for acoustically masking a tracking robot’s movements as it travels between observation locations selected to minimise its detectability by a dynamic natural target and ensuring con- tinuous visual tracking of the target. This work extends the overall concept by examining the utility of real-time acoustic signature self-assessment and exploiting shadows as hiding locations for use in a combined visual and acoustic stealth framework.

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This work is motivated by the desire to covertly track mobile targets, either animal or human, in previously unmapped outdoor natural environments using off-road robotic platforms with a non-negligible acoustic signature. The use of robots for stealthy surveillance is not new. Many studies exist but only consider the navigation problem to maintain visual covertness. However, robotic systems also have a significant acoustic footprint from the onboard sensors, motors, computers and cooling systems, and also from the wheels interacting with the terrain during motion. All these can jepordise any visual covertness. In this work, we experimentally explore the concepts of opportunistically utilizing naturally occurring sounds within outdoor environments to mask the motion of a robot, and being visually covert whilst maintaining constant observation of the target. Our experiments in a constrained outdoor built environment demonstrate the effectiveness of the concept by showing a reduced acoustic signature as perceived by a mobile target allowing the robot to covertly navigate to opportunistic vantage points for observation.

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We present a pole inspection system for outdoor environments comprising a high-speed camera on a vertical take-off and landing (VTOL) aerial platform. The pole inspection task requires a vehicle to fly close to a structure while maintaining a fixed stand-off distance from it. Typical GPS errors make GPS-based navigation unsuitable for this task however. When flying outdoors a vehicle is also affected by aerodynamics disturbances such as wind gusts, so the onboard controller must be robust to these disturbances in order to maintain the stand-off distance. Two problems must therefor be addressed: fast and accurate state estimation without GPS, and the design of a robust controller. We resolve these problems by a) performing visual + inertial relative state estimation and b) using a robust line tracker and a nested controller design. Our state estimation exploits high-speed camera images (100Hz) and 70Hz IMU data fused in an Extended Kalman Filter (EKF). We demonstrate results from outdoor experiments for pole-relative hovering, and pole circumnavigation where the operator provides only yaw commands. Lastly, we show results for image-based 3D reconstruction and texture mapping of a pole to demonstrate the usefulness for inspection tasks.

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This paper discusses the Townsville City Council Dry Tropics Water Smart (DTWS) initiative, developed by TCC Integrated Sustainability Services (ISS) and Townsville Water, and informed by The University of Adelaide. The program draws on many years of experience by the TCC team to blend key community-based research approaches in order to develop this residential outdoor water conservation program. Several community pilots have been conducted to test different behaviour change strategies and messages. This paper outlines recent steps taken to develop the community trials, as guided by a combination of behaviour change theories including community-based social marketing and thematic communications methods. Some preliminary results are outlined focused on community uptake of different strategies, community perceptions of communication materials, and some insights into the effectiveness of outdoor water hardware.

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This work investigated the impact of the HVAC filtration system and indoor particle sources on the relationship between indoor and outdoor airborne particle size and concentrations in an operating room. Filters with efficiency between 65% and 99.97% were used in the investigation and indoor and outdoor particle size and concentrations were measured. A balance mass model was used for the simulation of the impact of the surgical team, deposition rate, HVAC exhaust and air change rates on indoor particle concentration. The experimental results showed that high efficiency filters would not be expected to decrease the risk associated with indoor particles larger than approximately 1 µm in size because normal filters are relatively efficient for these large particles. A good fraction of outdoor particles were removed by deposition on the HVAC system surfaces and this deposition increased with particle size. For particles of 0.3-0.5 µm in diameter, particle reduction was about 23%, while for particles >10 µm the loss was about 78%. The modelling results showed that depending on the type of filter used, the surgical team generated between 93-99% of total particles, while the outdoor air contributed only 1-6%.

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Objectives: To assess the impact of exposure to ambient heat on urolithiasis among outdoor workers in a subtropical city of China. Methods: The 2003–2010 health check data of a shipbuilding company in Guangzhou, China were acquired. 190 cases and 760 matched controls were involved in this study. We assessed the relationship between exposure to ambient heat and urolithiasis for different occupations using conditional logistic regression. Results: Spray painters were most likely to develop urolithiasis (OR = 4.4; 95% CI: 1.7, 11.4), followed by smelter workers (OR = 4.0; 95% CI: 1.8, 9.2), welders (OR = 3.7; 95% CI: 1.9, 7.2), production security and quality inspectors (OR = 2.7; 95% CI: 1.4, 3.0), and assemblers (OR = 2.2; 95% CI: 1.1, 4.3). Overall, outdoor workers were more likely to present with urolithiasis compared with indoor employees (p b 0.05). In addition, workers with longer cumulative exposure time (OR = 1.5; 95% CI: 1.2, 1.8) and abnormal blood pressure (OR = 1.6; 95% CI: 1.0, 2.5) had higher risk for urolithiasis. Conclusions: Our findings demonstrate a significant association between exposure to ambient heat and urolithiasis among outdoor working populations. Public health intervention strategies should be developed to specifically target outdoor occupations.

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For robots operating in outdoor environments, a number of factors, including weather, time of day, rough terrain, high speeds, and hardware limitations, make performing vision-based simultaneous localization and mapping with current techniques infeasible due to factors such as image blur and/or underexposure, especially on smaller platforms and low-cost hardware. In this paper, we present novel visual place-recognition and odometry techniques that address the challenges posed by low lighting, perceptual change, and low-cost cameras. Our primary contribution is a novel two-step algorithm that combines fast low-resolution whole image matching with a higher-resolution patch-verification step, as well as image saliency methods that simultaneously improve performance and decrease computing time. The algorithms are demonstrated using consumer cameras mounted on a small vehicle in a mixed urban and vegetated environment and a car traversing highway and suburban streets, at different times of day and night and in various weather conditions. The algorithms achieve reliable mapping over the course of a day, both when incrementally incorporating new visual scenes from different times of day into an existing map, and when using a static map comprising visual scenes captured at only one point in time. Using the two-step place-recognition process, we demonstrate for the first time single-image, error-free place recognition at recall rates above 50% across a day-night dataset without prior training or utilization of image sequences. This place-recognition performance enables topologically correct mapping across day-night cycles.