This paper presents a vision-based underwater mapping system, which is demonstrated in an archaeological survey of the submerged ancient town of Pavlopetri. The snorkeler or diver operated system provides a low cost alternative to the use of an AUV or ROV in shallow waters. The system produces textured three-dimensional models, which contain significantly more information than traditional archaeological survey methods. The photo-realistic maps that are produced allow further archaeological research to be performed, without diving on a site during the restrictive time limitations of permits and field seasons. The hardware and software components of the mapping system and its method of operation are described, and initial results are presented and discussed.

Three closed-form solutions are proposed for six degree of freedom (6-DOF) visual odometry for light field cameras. The first approach breaks the problem into geometrically driven sub-problems with solutions adaptable to specific applications, while the second generalizes methods from optical flow to yield a more direct approach. The third solution integrates elements into a remarkably simple equation of plenoptic flow which is directly solved to estimate the camera's motion. The proposed methods avoid feature extraction, operating instead on all measured pixels, and are therefore robust to noise. The solutions are closed-form, computationally efficient, and operate in constant time regardless of scene complexity, making them suitable for real-time robotics applications. Results are shown for a simulated underwater survey scenario, and real-world results demonstrate good performance for a three-camera array, outperforming a state-of-the-art stereo feature-tracking approach.

This paper describes an approach to achieving high resolution, repeated benthic surveying using an Autonomous Underwater Vehicle (AUV). A stereo based Simultaneous Localisation and Mapping (SLAM) technique is used to estimate the trajectory of the vehicle during multiple overlapping grid based surveys. The vehicle begins each dive on the surface and uses GPS to navigate to a designated start location. Once it reaches the designated location on the surface, the vehicle dives and executes a pre-programmed grid survey, collecting co-registered high resolution stereo images, multibeam sonar and water chemistry data. A suite of navigation instruments are used while the vehicle is underway to estimate its pose relative to the local navigation frame. Following recovery of the vehicle, the SLAM technique is used to refine the estimated vehicle trajectory and to find loop closures both within each survey and between successive missions to co-register the dives. Results are presented from recent deployments of the AUV Sirius at a site in North Eastern Tasmania. The objective of the deployments described in this work were to document the behaviour of barrens-forming sea sea urchins which have recently become resident in the area. The sea urchins can overgraze luxuriant kelp beds that once dominated these areas, leaving only rocky barrens habitat. The high resolution stereo images and resulting three dimensional surface models allow the nocturnal behaviour of the animals, which emerge to feed predominantly at night, to be described. Co-registered images and resulting habitat models collected during the day and at night are being analysed to describe the behaviour of the sea urchins in more detail.

This paper presents a simultaneous localization and mapping algorithm suitable for large-scale visual navigation. The estimation process is based on the viewpoint augmented navigation (VAN) framework using an extended information filter. Cholesky factorization modifications are used to maintain a factor of the VAN information matrix, enabling efficient recovery of state estimates and covariances. The algorithm is demonstrated using data acquired by an autonomous underwater vehicle performing a visual survey of sponge beds. Loop-closure observations produced by a stereo vision system are used to correct the estimated vehicle trajectory produced by dead reckoning sensors.

This paper presents results of the application of the simultaneous localisation and mapping algorithm to data collected by an unmanned underwater vehicle operating on the Great Barrier Reef in Australia. By fusing information from the vehicle's on-board sonar and vision systems, it is possible to use the highly textured reef to provide estimates of the vehicle motion as well as to generate models of the gross structure of the underlying reefs. Terrain-aided navigation promises to revolutionise the ability of marine systems to track underwater bodies in many applications. This work represents a crucial step in the development of underwater technologies capable of long-term, reliable deployment. Results of the application of this technique to the tracking of the vehicle position are shown.