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 tasksManuscript received April 1, 2011; revised August 5, 2011; accepted October 7, 2011. Date of publication November 30, 2011; date of current version January 9, 2012. Recommended by Guest Editor W. Kirkwood. This work was supported in part by the TRIDENT EU FP7-Project under Grant ICT-248497, in part by the Marie Curie PERG-GA-2010-276778 (Surf3DSLAM), and in part by the Spanish Government under the projects DPI2008-06548-C03 and CTM2010-15216/MA

The goals of this field note are twofold. First, we detail the operations and discuss the results of the 2005 Chios ancient shipwreck survey. This survey was conducted by an international team of engineers, archaeologists and natural scientists off the Greek island of Chios in the northeastern Aegean Sea using an autonomous underwater vehicle (AUV) built specifically for high-resolution site inspection and characterization. Second, using the survey operations as context, we identify the specific challenges of adapting AUV technology for deep water archaeology and describe how our team addressed these challenges during the Chios expedition. After identifying the state-of-the-art in robotic tools for deep water archaeology, we discuss opportunities where new developments and research (e.g., AUV platforms, underwater imaging, remote sensing and navigation techniques) will improve rapid assessment of deep water archaeological sites. It is our hope that by reporting on the Chios field expedition we can both describe the opportunities that AUVs bring to fine resolution seafloor site surveys and elucidate future opportunities for collaborations between roboticists and ocean scientists.

In this field note we detail the operations and discuss the results of an experiment conducted in the unstructured environment of an underwater cave complex, using an autonomous underwater vehicle (AUV). For this experiment the AUV was equipped with two acoustic sonar to simultaneously map the caves' horizontal and vertical surfaces. Although the caves' spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan matching algorithm in a simultaneous localization and map- * The author can be also reached at Computer Vision and Robotics Institute, Universitat de Girona, 17003, Girona, Spain, amallios@eia.udg.edu.ping (SLAM) framework that significantly reduces and bounds the localization error for fully autonomous navigation. These methods are generalizable for AUV exploration in confined underwater environments where surfacing or pre-deployment of localization equipment are not feasible and may provide a useful step toward AUV utilization as a response tool in confined underwater disaster areas.

This paper proposes a pose-based algorithm to solve the full simultaneous localization and mapping problem for autonomous underwater vehicle (AUV) navigating in unknown and possibly unstructured environments. The proposed method first estimates the local path traveled by the robot while forming the acoustic image (scan) with range data coming from a mono-beam rotating sonar head, providing position estimates for correcting the distortions that the vehicle motion produces in the scans. Then, consecutive scans are cross-registered under a probabilistic scan matching technique for estimating the displacements of the vehicle including the uncertainty of the scan matching result. Finally, an augmented state extended Kalman filter estimates and keeps the registered scans poses. No prior structural information or initial pose are considered. The viability of the proposed approach has been tested reconstructing the trajectory of a guided AUV operating along a 600 m path within a marina environmentThis research work was partially sponsored by the Spanish project DPI2011-27977-C03-02 (COMAROB) and two European Commission's Seventh Framework Program 2007-2013 Projects: ICT-248497 (TRIDENT) and Marie Curie PERG-GA-2010-276778 (Surf3DSLAM). The dataset was acquired with the help of the members (staff and students) of the Computer Vision and Robotics research group at the University of Giron