Selective Submap Joining for underwater large scale 6-DOF SLAM
Selective Submap Joining for underwater large scale 6-DOF SLAM
复制标题
水下大规模六自由度 SLAM 的选择性子图连接
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Y. Pétillot
中科院分区：
文献类型：
--
作者：
J. Aulinas;X. Lladó;J. Salvi;Y. Pétillot
自主水下航行器（AUV）需要不同于全球定位系统（GPS）的定位系统，GPS在水下场景中不起作用。对于这种缺乏GPS信号的可能解决方案是同时定位和地图绘制（SLAM）算法。SLAM算法的目标是在构建地图的同时定位其中的车辆，这些算法在大规模场景面前受到一些限制。例如，它们不能为大面积区域绘制一致的地图，主要是因为不确定性随着场景的增加而增加。此外，计算成本随着地图的大小而增加。已经证明，使用局部映射降低了计算成本并提高了映射一致性。根据这一思想，在本文中，我们提出了一种新的SLAM技术的基础上使用独立的本地地图，结合全球级的随机地图。全局级包含局部映射之间的相对变换。一旦检测到新的循环并且局部图之间的重叠量高，则更新这些局部图。因此，共享大量特征的地图通过融合进行更新，保持地标和车辆之间的相关性。在REMUS-100 AUV上获得的真实的数据上的实验结果表明，该方法能够获得一致的大地图区域。
Autonomous Underwater Vehicles (AUVs) need positioning systems different than the Global Positioning System (GPS), which does not work in underwater scenarios. A possible solution to this lack of GPS signal are the Simultaneous Localization and Mapping (SLAM) algorithms. SLAM algorithms aim to build a map while simultaneously localize the vehicle within it. These algorithms suffer from several limitations in front of large scale scenarios. For instance, they do not perform consistent maps for large areas, mainly because uncertainties increase with the scenario. In addition, the computational cost increases with the map size. It has been demonstrated that the use of local maps reduces computational cost and improves map consistency. Following this idea, in this paper we propose a new SLAM technique based on using independent local maps, combined with a global level stochastic map. The global level contains the relative transformations between local maps. These local maps are updated once a new loop is detected and the amount of overlapping between local maps is high. Thus, maps sharing a high number of features are updated through fusion, maintaining the correlation between landmarks and vehicle. Experimental results on real data obtained from the REMUS-100 AUV show that our approach is able to obtain large map areas consistently.