分层无监督拓扑SLAM
Hierarchical Unsupervised Topological SLAM
AI总结：
该研究提出一种无监督拓扑聚类的分层框架，通过先拓扑层后图像层的分层回环发现策略，显著提升了基于图像的SLAM回环检测与闭合性能，且拓扑划分可支撑下游导航等任务。
摘要：
本文提出了一种用于无监督拓扑聚类的新型框架，可改进SLAM的回环检测与回环闭合性能。移动机器人在导航过程中将其行进路径聚类为视觉相似的拓扑结构，每个聚类（拓扑）包含一组通常从空间相邻位置观测到的外观相似的图像，这类由空间相邻且视觉相似的图像组成的分组，即为无需任何监督得到的拓扑结构。本文提出了一种分层回环发现策略：首先在拓扑层面检测回环，随后在存在回环的拓扑之间的图像层面检测回环。我们在不同Habitat环境中开展了多次行进路径实验，结果表明，这种分层流水线显著提升了当前最优（SOTA）的基于图像的回环检测与回环闭合方法的性能。此外，得益于回环检测效果的提升，我们也增强了回环闭合与后端SLAM的性能。这种将行进路径划分为拓扑片段的方式，对导航等下游任务有益：现在可以构建拓扑图，其中空间相邻的拓扑聚类通过边连接，机器人可在这类拓扑图上执行导航。
英文摘要：
In this paper we present a novel framework for unsupervised topological clustering resulting in improved loop. In this paper we present a novel framework for unsupervised topological clustering resulting in improved loop detection and closure for SLAM. A navigating mobile robot clusters its traversal into visually similar topologies where each cluster (topology) contains a set of similar looking images typically observed from spatially adjacent locations. Each such set of spatially adjacent and visually similar grouping of images constitutes a topology obtained without any supervision. We formulate a hierarchical loop discovery strategy that first detects loops at the level of topologies and subsequently at the level of images between the looped topologies. We show over a number of traversals across different Habitat environments that such a hierarchical pipeline significantly improves SOTA image based loop detection and closure methods. Further, as a consequence of improved loop detection, we enhance the loop closure and backend SLAM performance. Such a rendering of a traversal into topological segments is beneficial for downstream tasks such as navigation that can now build a topological graph where spatially adjacent topological clusters are connected by an edge and navigate over such topological graphs.
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