This paper outlines the specifications and basic design approach taken on the development of the Girona 500, an autonomous underwater vehicle whose most remarkable characteristic is its capacity to reconfigure for different tasks. The capabilities of this new vehicle range from different forms of seafloor survey to inspection and intervention tasks.

Girona 500 AUV: From Survey to Intervention

We have established an Australia-wide observation program that exhibits recent developments in autonomous underwater vehicle (AUV) systems to deliver precisely navigated time series benthic imagery at selected reference stations on Australia's continental shelf. These observations are designed to help characterize changes in benthic assemblage composition and cover derived from precisely registered maps collected at regular intervals. This information will provide researchers with the baseline ecological data necessary to make quantitative inferences about the long-term effects of climate change and human activities on the benthos. Incorporating a suite of observations that capitalize on the unique capabilities of AUVs into Australia's integrated marine observation system (IMOS) [1] is providing a critical link between oceanographic and benthic processes. IMOS is a nationally coordinated program designed to establish and maintain the research infrastructure required to support Australia's marine science research. It has, and will maintain, a strategic focus on the impact of major boundary currents on continental shelf environments, ecosystems, and biodiversity. The IMOS AUV facility observation program is designed to generate physical and biological observations of benthic variables that cannot be cost effectively obtained by other means.

Monitoring of Benthic Reference Sites: Using an Autonomous Underwater Vehicle

This paper presents an automated solution to the visual inspection problem of hydroelectric dams. A small autonomous underwater vehicle, controllable in four degrees of freedom (surge, sway, heave, and yaw), is used for autonomously exploring the wall of a dam. The robot is easily programmed using a mission control language. Missions are executed by an intelligent control architecture that guides the robot to follow a predefined path surveying the wall. During the mission, the robot gathers onboard navigation data synchronized with optical imagery, sonar, and absolute navigation data, obtained from a moored buoy equipped with an ultra-short baseline system. After the mission, the images of the wall are used to build a photomosaic of the inspected area. First, image features are matched over the image sequence. Then, navigation data and interimage correspondences are optimized together using bundle adjustment techniques. Thus, a georeferenced globally aligned set of images is obtained. Finally, a blending algorithm is used to obtain smooth seam transitions among the different images that constitute the mosaic, compensating for light artifacts and improving the visual perception of the scene. © 2010 Wiley Periodicals, Inc.

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Visual inspection of hydroelectric dams using an autonomous underwater vehicle

ReviewIntervention AUVs: The next challenge☆☆☆

Challenges and future trends in marine robotics

Seafloor imagery is a rich source of data for the study of biological and geological processes. Among several applications, still images of the ocean floor can be used to build image composites referred to as photo-mosaics. Photo-mosaics provide a wide-area visual representation of the benthos, and enable applications as diverse as geological surveys, mapping and detection of temporal changes in the morphology of biodiversity. We present an approach for creating globally aligned photo-mosaics using 3D position estimates provided by navigation sensors available in deep water surveys. Without image registration, such navigation data does not provide enough accuracy to produce useful composite images. Results from a challenging data set of the Lucky Strike vent field at the Mid Atlantic Ridge are reported.

/pdf/large-area-photo-mosaics-using-global-alignment-and-39p41rbp85.pdf

Large-Area Photo-Mosaics Using Global Alignment and Navigation Data

[1] We present a georeferenced photomosaic of the Lucky Strike hydrothermal vent field (Mid-Atlantic Ridge, 37°18′N). The photomosaic was generated from digital photographs acquired using the ARGO II seafloor imaging system during the 1996 LUSTRE cruise, which surveyed a ∼1 km2 zone and provided a coverage of ∼20% of the seafloor. The photomosaic has a pixel resolution of 15 mm and encloses the areas with known active hydrothermal venting. The final mosaic is generated after an optimization that includes the automatic detection of the same benthic features across different images (feature-matching), followed by a global alignment of images based on the vehicle navigation. We also provide software to construct mosaics from large sets of images for which georeferencing information exists (location, attitude, and altitude per image), to visualize them, and to extract data. Georeferencing information can be provided by the raw navigation data (collected during the survey) or result from the optimization obtained from image matching. Mosaics based solely on navigation can be readily generated by any user but the optimization and global alignment of the mosaic requires a case-by-case approach for which no universally software is available. The Lucky Strike photomosaics (optimized and navigated-only) are publicly available through the Marine Geoscience Data System (MGDS, http://www.marine-geo.org). The mosaic-generating and viewing software is available through the Computer Vision and Robotics Group Web page at the University of Girona (http://eia.udg.es/∼rafa/mosaicviewer.html).

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2008GC002204

Globally aligned photomosaic of the Lucky Strike hydrothermal vent field (Mid‐Atlantic Ridge, 37°18.5′N): Release of georeferenced data, mosaic construction, and viewing software

Many applications in mobile and underwater robotics employ 3D vision techniques for navigation and mapping. These techniques usually involve the extraction and 3D reconstruction of scene interest points. Nevertheless, in large environments the huge volume of acquired information could pose serious problems to real-time data processing. Moreover, In order to minimize the drift, these techniques use data association to close trajectory loops, decreasing the uncertainties in estimating the position of the robot and increasing the precision of the resulting 3D models. When faced to large amounts of features, the efficiency of data association decreases drastically, affecting the global performance. This paper proposes a framework that highly reduces the number of extracted features with minimum impact on the precision of the 3D scene model. This is achieved by minimizing the representation redundancy by analyzing the geometry of the environment and extracting only those features that are both photometrically and geometrically significant

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Identification of Suitable Interest Points Using Geometric and Photometric Cues in Motion Video for Efficient 3-D Environmental Modeling

This work extends a previously developed research concerning about the use of local model predictive control in differential driven mobile robots. Hence, experimental results are presented as a way to improve the methodology by considering aspects as trajectory accuracy and time performance. In this sense, the cost function and the prediction horizon are important aspects to be considered. The aim of the present work is to test the control method by measuring trajectory tracking accuracy and time performance. Moreover, strategies for the integration with perception system and path planning are briefly introduced. In this sense, monocular image data can be used to plan safety trajectories by using goal attraction potential fields.

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Local model predictive control experiences with differential driven wheeled mobile robots

Stereo triangulation lays at the basis of 3D scene recovery and it is used in a wide variety of areas ranging from urban modelling to robot localisation and mapping. However, triangulation produces non-Gaussian 3D estimates from Gaussian image measurements owing to its nonlinear nature. While previous work demonstrates the presence of statistical bias and how to correct the depth estimate, in this presented report, proposed and proven in a Monte Carlo test, is an enhancement for correcting the full 3D position given the image projection noise variance.

/pdf/bias-reduction-for-stereo-triangulation-4rfsnd13tz.pdf

J. Ferrer

Papers

Large-Area Photo-Mosaics Using Global Alignment and Navigation Data

Globally aligned photomosaic of the Lucky Strike hydrothermal vent field (Mid‐Atlantic Ridge, 37°18.5′N): Release of georeferenced data, mosaic construction, and viewing software

Identification of Suitable Interest Points Using Geometric and Photometric Cues in Motion Video for Efficient 3-D Environmental Modeling

Local model predictive control experiences with differential driven wheeled mobile robots

Bias reduction for stereo triangulation