论文标题
实时大规模大满贯的在线随机变异高斯流程映射
Online Stochastic Variational Gaussian Process Mapping for Large-Scale SLAM in Real Time
论文作者
论文摘要
在科学和工业应用中，自动驾驶水下车辆（AUV）正在成为水下勘探和海底映射的标准工具\ cite {graham20222rapid，stenius2022system}。他们潜水不受限制的能力使他们能够到达表面容器无法接近的区域，并可以更接近海底，而不论水深如何。但是，他们的导航自主权仍然受其死去的估算（DR）估算的准确性（DR）估算的准确性，在没有该地区和GPS信号的先验地图的情况下受到严重限制。与后者相当的全球本地化系统存在于水下领域，例如LBL或USBL。但是，它们涉及昂贵的外部基础设施，其可靠性随着到达AUV的距离而降低，使其不适合深海调查。
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.
