Online Stochastic Variational Gaussian Process Mapping for Large-Scale SLAM in Real Time
This addresses the challenge of accurate global positioning for AUVs in deep-sea surveys where GPS and prior maps are unavailable, though it appears incremental as an adaptation of existing methods to this domain.
The paper tackles the problem of autonomous underwater vehicle (AUV) navigation in large-scale SLAM without external infrastructure, achieving real-time performance with an online stochastic variational Gaussian process mapping method.
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.