Abstract
This paper proposes a solution for merging the measurements from two perpendicular profiling sonars with different beam-widths, in the context of underwater karst (cave) exploration and mapping. This work is a key step towards the development of a full 6D pose SLAM framework adapted to karst aquifer, where potential water turbidity disqualifies vision-based methods, hence relying on acoustic sonar measurements. Those environments have complex geometries which require 3D sensing. Wide-beam sonars are mandatory to cover previously seen surfaces but do not provide 3D measurements as the elevation angles are unknown. The approach proposed in this paper leverages the narrow-beam sonar measurements to estimate local karst surface with Gaussian process regression. The estimated surface is then further exploited to infer scaled-beta distributions of elevation angles from a wide-beam sonar. The pertinence of the method was validated through experiments on simulated environments. As a result, this approach allows one to benefit from the high coverage provided by wide-beam sonars without the drawback of loosing 3D information.
Highlights
The context of this paper is the development of robotic systems to explore karstic environments.Karsts are networks of underground natural conduits resulting from the dissolution of soluble rocks, limestone, dolomite and gypsum that drain groundwater on large scales
The first contribution of this paper is to propose a 3D Gaussian process regression adapted to karst environment and more generally applicable to approximately elliptic cylindrical shaped surfaces
We presented a method for probabilistic sonar scan merging in the context of underwater karstic exploration
Summary
Karsts are networks of underground natural conduits resulting from the dissolution of soluble rocks, limestone, dolomite and gypsum that drain groundwater on large scales. In order to have a better understanding of karst aquifers, it is essential to know the geometry of the flow paths, which may be difficult to acquire. This is done by cave divers with a limited range due to physiological constraints. An autonomous robotic solution would provide the capacity to go further and deeper in the karst conduit while acquiring dense information on the environment
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