This paper presents an automated approach to recovering the true color of objects on the seafloor in images collected from multiple perspectives by an autonomous underwater vehicle (AUV) during the construction of three‐dimensional (3D) seafloor models and image mosaics. When capturing images underwater, the water column induces several effects on light that are typically negligible in air, such as color‐dependent attenuation and backscatter. AUVs must typically carry artificial lighting when operating at depths below 20‐30 m; the lighting pattern generated is usually not spatially consistent. These effects cause problems for human interpretation of images, limit the ability of using color to identify benthic biota or quantify changes over multiple dives, and confound computer‐based techniques for clustering and classification. Our approach exploits the 3D structure of the scene generated using structure‐from‐motion and photogrammetry techniques to provide basic spatial data to an underwater image formation model. Parameters that are dependent on the properties of the water column are estimated from the image data itself, rather than using fixed in situ infrastructure, such as reflectance panels or detailed data on water constitutes. The model accounts for distance‐based attenuation and backscatter, camera vignetting and the artificial lighting pattern, recovering measurements of the true color (reflectance) and thus allows us to approximate the appearance of the scene as if imaged in air and illuminated from above. Our method is validated against known color targets using imagery collected in different underwater environments by two AUVs that are routinely used as part of a benthic habitat monitoring program.
Abstract-A UAV is tasked to explore an unknown environment and to map the features it finds, but must do so without the use of infrastructure based localisation systems such as GPS, or any a-prior terrain data. The UAV navigates using a statistical estimation technique known as Simultaneous Localisation And Mapping (SLAM) which allows for the simultaneous estimation of the location of the UAV as well as the location of the features it sees. SLAM offers a unique approach to vehicle localisation with potential applications including planetary exploration, or when GPS is denied (for example under intentional GPS jamming, or applications where GPS signals cannot be reached), but more importantly can be used to augment already existing systems to improve robustness to navigation failure.One key requirement for SLAM to work is that it must reobserve features, and this has two effects: firstly, the improvement of the location estimate of the feature; and secondly, the improvement of the location estimate of the platform because of the statistical correlations that link the platform to the feature. So our UAV has two options; should it explore more unknown terrain to find new features, or should it revisit known features to improve localisation quality. These options are instantiated into the online path planner for the UAV.In this paper we present the SLAM algorithm and evaluate two important properties about the algorithm which assist in developing a path planning module for the UAV. The first of these is the use of the probabilistic measure of 'Entropy' as an information-based measure of the certainty in the map and vehicle locations, and is used as a utility function for planning the UAVs trajectory and determining the order in which features in the map are observed. The second is an observability analysis of SLAM which presents the unobservable states which are dependent on vehicle maneuvers. The analysis dictates the type of manoeuvres required by the UAV whilst observing features in order to maintain accurate statistical estimates of the map and vehicle location. This has the effect of reducing the action space that the path planner needs to search over.Using these two properties, we demonstrate an online path planner that intelligently plans the vehicles trajectory while exploring unknown terrain in order to maximise the quality of both the map and vehicle location. Results of the online path planning algorithm are presented using a 6-DoF simulator of our UAV. The results show that the vehicle localisation errors are constrained and that the number of features and the size of the map steadily grows during the flight.
Habitat structural complexity is a key factor shaping marine communities. However, accurate methods for quantifying structural complexity underwater are currently lacking. Loss of structural complexity is linked to ecosystem declines in biodiversity and resilience. We developed new methods using underwater stereo-imagery spanning 4 years (2010-2013) to reconstruct 3D models of coral reef areas and quantified both structural complexity at two spatial resolutions (2.5 and 25 cm) and benthic community composition to characterize changes after an unprecedented thermal anomaly on the west coast of Australia in 2011. Structural complexity increased at both resolutions in quadrats (4 m(2)) that bleached, but not those that did not bleach. Changes in complexity were driven by species-specific responses to warming, highlighting the importance of identifying small-scale dynamics to disentangle ecological responses to disturbance. We demonstrate an effective, repeatable method for quantifying the relationship among community composition, structural complexity and ocean warming, improving predictions of the response of marine ecosystems to environmental change.
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