Online Stochastic Variational Gaussian Process Mapping for Large-Scale SLAM in Real Time

• URL: http://arxiv.org/abs/2211.05601v1
• Date: Thu, 10 Nov 2022 14:21:48 GMT
• Title: Online Stochastic Variational Gaussian Process Mapping for Large-Scale SLAM in Real Time
• Authors: Ignacio Torroba, Marco Chella, Aldo Teran, Niklas Rolleberg, John Folkesson
• Abstract summary: AUVs are becoming standard tools for underwater exploration and seabed mapping in both scientific and industrial applications. Their capacity to dive untethered allows them to reach areas inaccessible to surface vessels and to collect data more closely to the seafloor. navigation autonomy remains bounded by the accuracy of their dead reckoning (DR) estimate of their global position, severely limited in the absence of a priori maps of the area and GPS signal.
• Score: 1.3387004254920498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous underwater vehicles (AUVs) are becoming standard tools for underwater exploration and seabed mapping in both scientific and industrial applications \cite{graham2022rapid, stenius2022system}. Their capacity to dive untethered allows them to reach areas inaccessible to surface vessels and to collect data more closely to the seafloor, regardless of the water depth. However, their navigation autonomy remains bounded by the accuracy of their dead reckoning (DR) estimate of their global position, severely limited in the absence of a priori maps of the area and GPS signal. Global localization systems equivalent to the later exists for the underwater domain, such as LBL or USBL. However they involve expensive external infrastructure and their reliability decreases with the distance to the AUV, making them unsuitable for deep sea surveys.

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