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The measurement of sustainability can provide significant information in order to plan and implement environmental policies. Several methods have been proposed in the literature in order to select the appropriate indicators along with a variety of techniques for their measurement. These methods are often divided in two main categories: a) a top-down approach where the selection of sustainability indicators is often based on the decision of scientists and policy-makers and b) a bottom-up approach where local stakeholders influence the selection and the measurement of indicators. In this paper we propose a methodological framework which allows the selection of sustainability indicators based on scientific research but at the same time allows citizens to influence both the selection of indicators and their measurement. The benefits of the specific methodological framework are two: a) to incorporate in different stages of the measurement the opinions of local stakeholders, b) to assist in policy decision-making through the assessment of the current situation of sustainability.

Proteins in barley grains are determinants of beer quality, which are modified during malting to provide nutrition for yeast during brewing. Different malting barley varieties behave differently during malting. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used to characterize storage proteins including albumin, globulin, hordein and glutelin in grains and malt of parental varieties and a doubled haploid (DH) population. Each parental variety produces malt with particular quality profiles. The protein profiles of analyzed extracts in seed and malt were compared to find the most influenced proteins during the malting process. Our results revealed that malting procedure dramatically affected the composition and amounts of albumin, then hordein, glutelin and globulin in barley. Moreover, hordein and glutelin profiles in the DH population exhibited high levels of polymorphism. The genetic information represented by individual hordein and glutelin profiles was successfully mapped as molecular markers in a genetic linkage map. Twelve and 8 (hordein in seed and malt, respectively) or 10 and 8 (glutelin in seed and malt, respectively) segregating peaks were scored as polymorphic across the population, and all peaks were located to the chromosome 1H. The MALDI-TOF MS provides a method with high resolution and throughput to characterize mass patterns of extracted storage proteins in barley and malt.

Prediction of malt fermentability (apparent attenuation limit — AAL) by measurement of the diastatic power enzymes (DPE), α-amylase, total limit dextrinase, total β-amylase, β-amylase thermostability, and the Kolbach index (KI or free amino nitrogen — FAN) is superior to the conventional use of diastatic power (DP) alone. The thermostability of β-amylase is known to be an important factor in determining fermentability, thus the thermostability of the other relatively thermolabile enzyme, limit dextrinase, was investigated to determine if it was also useful in predicting fermentability. To facilitate this aim, methods were developed for a rapid and cost efficient assay of both β-amylase and limit dextrinase thermostability. Internationally important Australian and international malting varieties were compared for their total limit dextrinase and β-amylase activity and thermostability. Interestingly, the level of limit dextrinase thermostability was observed to be inversely correlated with total limit dextrinase activity. The prediction of malt fermentability was achieved by both forward step-wise multi-linear regression (MLR) and the partial least squares (PLS) multivariate model development methods. Both methods produced similar identifications of the parameters predicting wort fermentability at similar levels of predictive power. Both models were substantially better at predicting fermentability than the traditional use of DP on its own. The emphasis of this study was on the identification of predictive factors that can be consistently used in models to predict fermentability, because the model parameter estimates will subtly vary depending on mashing conditions, yeast strain/fermentation conditions and malt source. The application of these multivariate model development methods (PLS and MLR) enabled the identification of further potential fermentability predicting factors. The analyses divided the predictive parameters into those defined by DP enzymes and those as

Current state of the art solutions of the SLAM problem are based on efficient sparse optimization techniques and represent the problem as probabilistic constraint graphs. For example in pose graphs the nodes represent poses and the edges between them express spatial information (e.g. obtained from odometry) and information on loop closures. The task of constructing the graph is delegated to a front-end that has access to the available sensor information. The optimizer, the so called back-end of the system, relies heavily on the topological correctness of the graph structure and is not robust against misplaced constraint edges. Especially edges representing false positive loop closures will lead to the divergence of current solvers. We propose a novel formulation that allows the back-end to change parts of the topological structure of the graph during the optimization process. The back-end can thereby discard loop closures and converge towards correct solutions even in the presence of false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. We demonstrate the approach and present results both on large scale synthetic and real-world datasets.

For humans and robots to communicate using natural language it is necessary for the robots to develop concepts and associated terms that correspond to the human use of words. Time and space are foundational concepts in human language, and to develop a set of words that correspond to human notions of time and space, it is necessary to take into account the way that they are used in natural human conversations, where terms and phrases such as `soon', `in a while', or `near' are often used. We present language learning robots called Lingodroids that can learn and use simple terms for time and space. In previous work, the Lingodroids were able to learn terms for space. In this work we extend their abilities by adding temporal variables which allow them to learn terms for time. The robots build their own maps of the world and interact socially to form a shared lexicon for location and duration terms. The robots successfully use the shared lexicons to communicate places and times to meet again.

This paper describes system identification, estimation and control of translational motion and heading angle for a cost effective open-source quadcopter — the MikroKopter. The dynamics of its built-in sensors, roll and pitch attitude controller, and system latencies are determined and used to design a computationally inexpensive multi-rate velocity estimator that fuses data from the built-in inertial sensors and a low-rate onboard laser range finder. Control is performed using a nested loop structure that is also computationally inexpensive and incorporates different sensors. Experimental results for the estimator and closed-loop positioning are presented and compared with ground truth from a motion capture system.

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.

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

This paper describes a novel method for determining the extrinsic calibration parameters between 2D and 3D LIDAR sensors with respect to a vehicle base frame. To recover the calibration parameters we attempt to optimize the quality of a 3D point cloud produced by the vehicle as it traverses an unknown, unmodified environment. The point cloud quality metric is derived from Rényi Quadratic Entropy and quantifies the compactness of the point distribution using only a single tuning parameter. We also present a fast approximate method to reduce the computational requirements of the entropy evaluation, allowing unsupervised calibration in vast environments with millions of points. The algorithm is analyzed using real world data gathered in many locations, showing robust calibration performance and substantial speed improvements from the approximations.

Appearance-based loop closure techniques, which leverage the high information content of visual images and can be used independently of pose, are now widely used in robotic applications. The current state-of-the-art in the field is Fast Appearance-Based Mapping (FAB-MAP) having been demonstrated in several seminal robotic mapping experiments. In this paper, we describe OpenFABMAP, a fully open source implementation of the original FAB-MAP algorithm. Beyond the benefits of full user access to the source code, OpenFABMAP provides a number of configurable options including rapid codebook training and interest point feature tuning. We demonstrate the performance of OpenFABMAP on a number of published datasets and demonstrate the advantages of quick algorithm customisation. We present results from OpenFABMAP’s application in a highly varied range of robotics research scenarios.

In this paper, we examine the use of a Kalman filter to aid in the mission planning process for autonomous gliders. Given a set of waypoints defining the planned mission and a prediction of the ocean currents from a regional ocean model, we present an approach to determine the best, constant, time interval at which the glider should surface to maintain a prescribed tracking error, and minimizing time on the ocean surface. We assume basic parameters for the execution of a given mission, and provide the results of the Kalman filter mission planning approach. These results are compared with previous executions of the given mission scenario.

We present an iterative hierarchical algorithm for multi-view stereo. The algorithm attempts to utilise as much contextual information as is available to compute highly accurate and robust depth maps. There are three novel aspects to the approach: 1) firstly we incrementally improve the depth fidelity as the algorithm progresses through the image pyramid; 2) secondly we show how to incorporate visual hull information (when available) to constrain depth searches; and 3) we show how to simultaneously enforce the consistency of the depth-map by continual comparison with neighbouring depth-maps. We show that this approach produces highly accurate depth-maps and, since it is essentially a local method, is both extremely fast and simple to implement.

Autonomous guidance of agricultural vehiclesis vital as mechanized farming production becomes more prevalent. It is crucial that tractor-trailers are guided with accuracy in both lateral and longitudinal directions, whilst being affected by large disturbance forces, or slips, owing to uncertain and undulating terrain. Successful research has been concentrated on trajectory control which can provide longitudinal and lateral accuracy if the vehicle moves without sliding, and the trailer is passive. In this paper, the problem of robust trajectory tracking along straight and circular paths of a tractor-steerable trailer is addressed. By utilizing a robust combination of backstepping and nonlinear PI control, a robust, nonlinear controller is proposed. For vehicles subjected to sliding, the proposed controller makes the lateral deviations and the orientation errors of the tractor and trailer converge to a neighborhood near the origin. Simulation results are presented to illustrate that the suggested controller ensures precise trajectory tracking in the presence of slip.