833 resultados para Design Economic aspects New South Wales Northern Rivers Region
Resumo:
Construction contracts often provide that decisions under the contract will be made by a certifier. This paper reviews the liability issues when a certifier makes a mistake. We do that in light of recent pronouncements by the High Court of Australia and the New South Wales Court of Appeal on negligence. We look at this question in the context of traditional construction contract arrangements and also consider the implications for Public Private Partnerships and the typical contract arrangements entered into to facilitate these transactions.
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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.
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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].
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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.
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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.
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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.
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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.
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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.
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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.
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We provide a taxonomic redescription of the ubiquitous and variable dasyurid marsupial Yellow-footed Antechinus, Antechinus flavipes (Waterhouse), which comprises three currently recognized subspecies whose combined geographic distribution spans almost the length and breadth of Australia. A. flavipes leucogaster Gray is confined to south-west Western Australia; A. flavipes flavipes is distributed in south-eastern Australia across four states—South Australia, Victoria, New South Wales and Queensland; A. flavipes rubeculus Van Dyck is confined to the wet tropics of Queensland. A. flavipes is readily distinguished from all extant congeners based on external morphology by the following combination of features: a grey head; orange-yellow toned flanks/rump, feet and tail base; pale eye-rings and a darkened tail tip. A. flavipes skulls are stout, being broad at the level of the rear upper molars, have small palatal vacuities and small entoconid cusps on the lower molars. However, notable differences among subspecies of A. flavipesprevent any obvious collection of skull characters being diagnostic for species-level discrimination among congeners. A. flavipes rubeculus is the largest of the three subspecies of Yellow-footed Antechinus and most similar in skull morphology to A. leo, A. bellus and A. godmani—all four species are geographically limited to tropical Australia. A. f. rubeculus is notably larger in many characters than its conspecifics: A. f. flavipes, the next largest, and A. f. leucogaster, the smallest of the group. A. f. flavipes and A. f. leucogaster diverge significantly at only a few skull characters, and both subspecies have cranial morphological affinities with the recently discovered A. mysticus, most notably A. f. leucogaster. Phylogenies generated from mt- and nDNA data strongly support Antechinus flavipes as monophyletic with respect to other members of the genus; within A. flavipes, each of the three recognized subspecies form distinctive monophyletic clades.
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This recent decision of the New South Wales Court of Appeal considers the scope of the parens patriae jurisdiction in cases where the jurisdiction is invoked for the protection of a Gillick competent minor. As outlined below, in certain circumstances the law recognises that mature minors are able to make their own decisions concerning medical treatment. However, there have been a number of Commonwealth decisions which have addressed the issue of whether mature minors are able to refuse medical procedures in circumstances where refusal will result in the minor dying. Ultimately, this case confirms that the minor does not necessarily have a right to make autonomous decisions; the minor’s right to exercise his or her autonomous decision only exists when such decision accords with what is deemed to be in his or her best interests.
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Courts set guidelines for when genetic testing would be ordered - medical testing - life insurers - use of test results - confidentiality.
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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.
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A number of recent developments in the United State (US), United Kingdom (UK) and Australia suggest that conditions may be ripe for a political shift in the reliance on escalating rates of imprisonment as a default criminal justice strategy for responding to crime. The default position is illustrated by the Yabsleyite response of former New South Wales (NSW) Premier Nathan Rees’s to questioning over the cost of prison building and NSW’s high recidivism rate: ‘[t]he advice to me is we have still got 500 cells empty, I don't mind if we fill them up, and if we fill them up and have to build another jail, we'll build another jail’ (Knox and Tadros 2008)...