SLAM for autonomous planetary rovers with global localization

This paper describes a novel approach to simultaneous localization and mapping (SLAM) techniques applied to the autonomous planetary rover exploration scenario to reduce both the relative and absolute localization errors, using two well‐proven techniques: particle filters and scan matching. Continuous relative localization is improved by matching high‐resolution sensor scans to the online created local map. Additionally, to avoid issues with drifting localization, absolute localization is globally corrected at discrete times, according to predefined event criteria, by matching the current local map to the orbiter's global map. The resolutions of local and global maps can be appropriately chosen for computation and accuracy purposes. Further, the online generated local map, of the form of a structured elevation grid map, can also be used to evaluate the traversability of the surrounding environment and allow for continuous navigation. The objective of this study is to support long‐range low‐supervision planetary exploration. The implemented SLAM technique has been validated with a data set acquired during a field test campaign performed at the Teide Volcano on the island of Tenerife, representative of a Mars/Moon exploration scenario.     

Error statistics for slope and aspect when derived frominterpolated data

Error models of slope and aspect of a terrain are presented. Such data are often extracted from a geographic information system (GIS), which may contain information from digital maps and remote sensing images. Although the sources of these data are usually of diverse resolution, all of them are usually resampled to refer to the same resolution. The authors examine the error associated with such data because of subsampling. The error distributions are modeled empirically     

A Bayesian Multiple Models Combination Method for Time Series Prediction

In this paper we present the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for time series prediction. BCP utilizes local predictors of several types (e.g., linear predictors, artificial neural network predictors, polynomial predictors etc.) and produces a final prediction which is a weighted combination of the local predictions; the weights can be interpreted as Bayesian posterior probabilities and are computed online. Two examples of the method are given, based on real world data: (a) short term load forecasting for the Greek Public Power Corporation dispatching center of the island of Crete, and (b) prediction of sugar beet yield based on data collected from the Greek Sugar Industry. In both cases, the BCP outperforms conventional predictors.     

Segmentation of color textures

This paper describes an approach to perceptual segmentation of color image textures. A multiscale representation of the texture image, generated by a multiband smoothing algorithm based on human psychophysical measurements of color appearance is used as the input. Initial segmentation is achieved by applying a clustering algorithm to the image at the coarsest level of smoothing. The segmented clusters are then restructured in order to isolate core clusters, i.e., patches in which the pixels are definitely associated with the same region. The image pixels representing the core clusters are used to form 3D color histograms which are then used for probabilistic assignment of all other pixels to the core clusters to form larger clusters and categorise the rest of the image. The process of setting up color histograms and probabilistic reassignment of the pixels to the clusters is then propagated through finer levels of smoothing until a full segmentation is achieved at the highest level of resolution     

Nonnegative tensor factorization as an alternative Csiszar–Tusnady procedure: algorithms,convergence, probabilistic interpretations and novel probabilistic tensor latent variable analysis algorithms

In this paper we study Nonnegative Tensor Factorization (NTF) based on the Kullback–Leibler (KL) divergence as an alternative Csiszar–Tusnady procedure. We propose new update rules for the aforementioned divergence that are based on multiplicative update rules. The proposed algorithms are built on solid theoretical foundations that guarantee that the limit point of the iterative algorithm corresponds to a stationary solution of the optimization procedure. Moreover, we study the convergence properties of the optimization procedure and we present generalized pythagorean rules. Furthermore, we provide clear probabilistic interpretations of these algorithms. Finally, we discuss the connections between generalized Probabilistic Tensor Latent Variable Models (PTLVM) and NTF, proposing in that way algorithms for PTLVM for arbitrary multivariate probabilistic mass functions.     

The Shadow Function for Rough Surfaces

For many remote sensing applications it is beneficial to know how the amount of shadows on a surface depends on illumination. Many natural surfaces (planetary surfaces being an example) may be successfully described by a fractal model. While the fractal shadowing function can be easily calculated experimentally, to date no rigorous analytical model of self-shadowing on a fractal surface exists. In this paper we offer an integral form of the shadowing function for fractal surfaces with different fractal and roughness parameters. The analysis is based on working out the covariance matrix for an arbitrarily long sequence of heights in a fractal profile.Svetlana Barsky received her BSc degree in Mathematics and Applied Mathematics from Novosibirsk State University, Russia, in 1992, and her MSc and PhD degrees from the University of Surrey, UK, in 1999 and 2003 respectively. Since then she has been working as a research fellow at the Centre for Vision, Speech and Signal Processing of the School of Electronics and Physical Sciecnes of Surrey University.Maria Petrou studied Physics at the Aristotle University of Thessaloniki, Greece, Applied Mathematics in Cambridge and she did her PhD in the Institute of Astronomy in Cambridge, UK. She has been working on image processing and computer vision since 1986. She has been the Professor of Image Analysis since 1998 and leads a group of 20 researchers on this topic in Surrey University. She has published more than 250 scientific papers, on Astronomy, Remote Sensing, Computer Vision, Machine Learning, Colour analysis, Industrial Inspection, Medical Signal and Image Processing. She has co-authored a book “Image Processing: the fundamentals” published by John Wiley in 1999 and reprinted in 2000 and 2003, and numerous book chapters. She is a Fellow of the Royal Academy of Engineering, Fellow of IEE and Fellow of IAPR. She has served as the Chairman of the Technical Committee for Remote Sensing of IAPR, the Chairman of the British Machine Vision Association (BMVA), as an Associate Editor of IEEE Transactions on Image Processing, as the Newsletter Editor of IAPR and is currently the treasurer of IAPR and an Honorary Editor of IEE Electronics Letters. A full list of publications and other details can be found in     

The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows

We present an algorithm for separating the local gradient information and Lambertian color by using 4-source color photometric stereo in the presence of highlights and shadows. We assume that the surface reflectance can be approximated by the sum of a Lambertian and a specular component. The conventional photometric method is generalized for color images. Shadows and highlights in the input images are detected using either spectral or directional cues and excluded from the recovery process, thus giving more reliable estimates of local surface parameters.     

Minimization of MRF Energy with Relaxation Labeling

Recently, there has been increasing interest in Markovrandom field (MRF) modeling for solving a variety of computer visionproblems formulated in terms of the maximum a posteriori(MAP) probability. When the label set is discrete, such as in imagesegmentation and matching, the minimization is combinatorial. Theobjective of this paper is twofold: Firstly, we propose to use thecontinuous relaxation labeling (RL) as an alternative approach forthe minimization. The motivation is that it provides a goodcompromise between the solution quality and the computational cost.We show how the original combinatorial optimization can be convertedinto a form suitable for continuous RL. Secondly, we compare variousminimization algorithms, namely, the RL algorithms proposed byRosenfeld et al., and by Hummel and Zucker, the mean field annealing ofPeterson and Soderberg, simulated annealing of Kirkpatrick, theiterative conditional modes (ICM) of Besag and an annealing versionof ICM proposed in this paper. The comparisons are in terms of theminimized energy value (i.e., the solution quality), the requirednumber of iterations (i.e., the computational cost), and also thedependence of each algorithm on heuristics.     

Comparison of Three Flexural Retrofit Systems under Monotonic and Fatigue Loads

The majority of experimental work involving the flexural retrofit of concrete bridge girders has been conducted on beam specimens with adhesive-applied, soffit-mounted, fiber-reinforced polymer (FRP) composite systems, referred to in this study as conventional adhesive application (CAA). It has been observed that the performance of such girders is often controlled by the quality of the bond between the FRP and the concrete substrate and the substrate’s ability to transfer stress from the steel to the FRP. With the goal of improving the performance of bonded FRP in mind, two additional soffit-mounted retrofit schemes are investigated: near-surface mounted (NSM), where the FRP strips are embedded in adhesive within slots cut into the substrate concrete, and, powder-actuated fastener-applied (PAF) FRP, which uses a powder-actuated nail gun to install mechanical fasteners through predrilled holes in the FRP into the concrete substrate, “nailing” the FRP in place. The PAF application is a recent development, and little work has been done on it other than by the proprietors of the system. This study reports on a comparative study of the static and fatigue performance of reinforced concrete beams retrofitted with CAA, NSM, and PAF FRP retrofit systems. Ten medium-scale beams were tested: six strengthened specimens, two per retrofit method, were tested under cyclic loading conditions, and four specimens, one per retrofit method and one control specimen, were tested monotonically to failure. The results of this study indicate that although all three methods of FRP application result in significant strength increases over the control specimen under monotonic loading conditions, the CAA method is outperformed by the other methods under cyclic conditions. A number of other relevant detailed conclusions with respect to performance and practical application issues are presented for each of the methods of retrofit examined in this study. Significantly, clear evidence of FRP debonding in the midspan region prior to specimen failure is presented.