Vision-based localization of an underwater robot in a structured environment

This paper presents a vision-based localization approach for an underwater robot in a structured environment. The system is based on a coded pattern placed on the bottom of a water tank and an onboard down looking camera. Main features are, absolute and map-based localization, landmark detection and tracking, and real-time computation (12.5 Hz). The proposed system provides three-dimensional position and orientation of the vehicle along with its velocity. Accuracy of the drift-free estimates is very high, allowing them to be used as feedback measures of a velocity-based low-level controller. The paper details the localization algorithm, by showing some graphical results, and the accuracy of the system

ICTINEUAUV wins the First SAUC-E competition

A pioneer team of students of the University of Girona decided to design and develop an autonomous underwater vehicle (AUV) called ICTINEU-AUV to face the Student Autonomous Underwater Challenge-Europe (SAUC-E). The prototype has evolved from the initial computer aided design (CAD) model to become an operative AUV in the short period of seven months. The open frame and modular design principles together with the compatibility with other robots previously developed at the lab have provided the main design philosophy. Hence, at the robot's core, two networked computers give access to a wide set of sensors and actuators. The Gentoo/Linux distribution was chosen as the onboard operating system. A software architecture based on a set of distributed objects with soft real time capabilities was developed and a hybrid control architecture including mission control, a behavioural layer and a robust map-based localization algorithm made ICTINEU-AUV the winning entry

Localització indoor a través de dispositius Android

Durant el projecte s'ha planificat i dut a terme la creació i verificació d'un APP que simula una aplicació comercial senzilla. S'ha seleccionat una localització, el centre comercial l'Illa Diagonal, s'ha calibrat seguint les instruccions de WiFiSLAM, s'ha creat un APP que integra la visualització de la posició sobre plànol basada en el SDK de WiFiSLAM amb funcions de càlcul de distàncies i s'han fet proves per acotar la fiabilitat de la solució, la seva exactitud i la seva precisió.

Analysis of meiotic recombination in 22q11.2, a region that frequently undergoes deletions and duplications

Background: The 22q11.2 deletion syndrome is the most frequent genomic disorder with an estimated frequency of 1/4000 live births. The majority of patients (90%) have the same deletion of 3 Mb (Typically Deleted Region, TDR) that results from aberrant recombination at meiosis between region specific low-copy repeats (LCRs). Methods: As a first step towards the characterization of recombination rates and breakpoints within the 22q11.2 region we have constructed a high resolution recombination breakpoint map based on pedigree analysis and a population-based historical recombination map based on LD analysis. Results: Our pedigree map allows the location of recombination breakpoints with a high resolution (potential recombination hotspots), and this approach has led to the identification of 5 breakpoint segments of 50 kb or less (8.6 kb the smallest), that coincide with historical hotspots. It has been suggested that aberrant recombination leading to deletion (and duplication) is caused by low rates of Allelic Homologous Recombination (AHR) within the affected region. However, recombination rate estimates for 22q11.2 region show that neither average recombination rates in the 22q11.2 region or within LCR22-2 (the LCR implicated in most deletions and duplications), are significantly below chromosome 22 averages. Furthermore, LCR22-2, the repeat most frequently implicated in rearrangements, is also the LCR22 with the highest levels of AHR. In addition, we find recombination events in the 22q11.2 region to cluster within families. Within this context, the same chromosome recombines twice in one family; first by AHR and in the next generation by NAHR resulting in an individual affected with the del22q11.2 syndrome. Conclusion: We show in the context of a first high resolution pedigree map of the 22q11.2 region that NAHR within LCR22 leading to duplications and deletions cannot be explained exclusively under a hypothesis of low AHR rates. In addition, we find that AHR recombination events cluster within families. If normal and aberrant recombination are mechanistically related, the fact that LCR22s undergo frequent AHR and that we find familial differences in recombination rates within the 22q11.2 region would have obvious health-related implications.

An approach to vision-based station keeping for an unmanned underwater vehicle

This paper presents an automatic vision-based system for UUV station keeping. The vehicle is equipped with a down-looking camera, which provides images of the sea-floor. The station keeping system is based on a feature-based motion detection algorithm, which exploits standard correlation and explicit textural analysis to solve the correspondence problem. A visual map of the area surveyed by the vehicle is constructed to increase the flexibility of the system, allowing the vehicle to position itself when it has lost the reference image. The testing platform is the URIS underwater vehicle. Experimental results demonstrating the behavior of the system on a real environment are presented

Monocular-based 3-D seafloor reconstruction and ortho-mosaicing by piecewise planar representation

Photo-mosaicing techniques have become popular for seafloor mapping in various marine science applications. However, the common methods cannot accurately map regions with high relief and topographical variations. Ortho-mosaicing borrowed from photogrammetry is an alternative technique that enables taking into account the 3-D shape of the terrain. A serious bottleneck is the volume of elevation information that needs to be estimated from the video data, fused, and processed for the generation of a composite ortho-photo that covers a relatively large seafloor area. We present a framework that combines the advantages of dense depth-map and 3-D feature estimation techniques based on visual motion cues. The main goal is to identify and reconstruct certain key terrain feature points that adequately represent the surface with minimal complexity in the form of piecewise planar patches. The proposed implementation utilizes local depth maps for feature selection, while tracking over several views enables 3-D reconstruction by bundle adjustment. Experimental results with synthetic and real data validate the effectiveness of the proposed approach

GIS-based debris flow source and runout susceptibility assessment from DEM data - a case study in NW Nicaragua

In October 1998, Hurricane Mitch triggered numerous landslides (mainly debris flows) in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. The potential application of relatively simple and affordable spatial prediction models for landslide hazard mapping in developing countries was studied. Our attention was focused on a region in NW Nicaragua, one of the most severely hit places during the Mitch event. A landslide map was obtained at 1:10 000 scale in a Geographic Information System (GIS) environment from the interpretation of aerial photographs and detailed field work. In this map the terrain failure zones were distinguished from the areas within the reach of the mobilized materials. A Digital Elevation Model (DEM) with 20 m×20 m of pixel size was also employed in the study area. A comparative analysis of the terrain failures caused by Hurricane Mitch and a selection of 4 terrain factors extracted from the DEM which, contributed to the terrain instability, was carried out. Land propensity to failure was determined with the aid of a bivariate analysis and GIS tools in a terrain failure susceptibility map. In order to estimate the areas that could be affected by the path or deposition of the mobilized materials, we considered the fact that under intense rainfall events debris flows tend to travel long distances following the maximum slope and merging with the drainage network. Using the TauDEM extension for ArcGIS software we generated automatically flow lines following the maximum slope in the DEM starting from the areas prone to failure in the terrain failure susceptibility map. The areas crossed by the flow lines from each terrain failure susceptibility class correspond to the runout susceptibility classes represented in a runout susceptibility map. The study of terrain failure and runout susceptibility enabled us to obtain a spatial prediction for landslides, which could contribute to landslide risk mitigation.

On the bicanonical map of irregular varieties

From the point of view of uniform bounds for the birationality of pluricanonical maps, irregular varieties of general type and maximal Albanese dimension behave similarly to curves. In fact Chen-Hacon showed that, at least when their holomorphic Euler characteristic is positive, the tricanonical map of such varieties is always birational. In this paper we study the bicanonical map. We consider the natural subclass of varieties of maximal Albanese dimension formed by primitive varieties of Albanese general type. We prove that the only such varieties with non-birational bicanonical map are the natural higher-dimensional generalization to this context of curves of genus $2$: varieties birationally equivalent to the theta-divisor of an indecomposable principally polarized abelian variety. The proof is based on the (generalized) Fourier-Mukai transform.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

On the bicanonical map of irregular varieties

From the point of view of uniform bounds for the birationality of pluricanonical maps, irregular varieties of general type and maximal Albanese dimension behave similarly to curves. In fact Chen-Hacon showed that, at least when their holomorphic Euler characteristic is positive, the tricanonical map of such varieties is always birational. In this paper we study the bicanonical map. We consider the natural subclass of varieties of maximal Albanese dimension formed by primitive varieties of Albanese general type. We prove that the only such varieties with non-birational bicanonical map are the natural higher-dimensional generalization to this context of curves of genus $2$: varieties birationally equivalent to the theta-divisor of an indecomposable principally polarized abelian variety. The proof is based on the (generalized) Fourier-Mukai transform.

Local map update for large scale SLAM

A technique for simultaneous localisation and mapping (SLAM) for large scale scenarios is presented. This solution is based on the use of independent submaps of a limited size to map large areas. In addition, a global stochastic map, containing the links between adjacent submaps, is built. The information in both levels is corrected every time a loop is closed: local maps are updated with the information from overlapping maps, and the global stochastic map is optimised by means of constrained minimisation

Viewpoint-Based Ambient Occlusion

We've developed a new ambient occlusion technique based on an information-theoretic framework. Essentially, our method computes a weighted visibility from each object polygon to all viewpoints; we then use these visibility values to obtain the information associated with each polygon. So, just as a viewpoint has information about the model's polygons, the polygons gather information on the viewpoints. We therefore have two measures associated with an information channel defined by the set of viewpoints as input and the object's polygons as output, or vice versa. From this polygonal information, we obtain an occlusion map that serves as a classic ambient occlusion technique. Our approach also offers additional applications, including an importance-based viewpoint-selection guide, and a means of enhancing object features and producing nonphotorealistic object visualizations