Recognizing already explored places (a.k.a. place recognition) is a fundamental task in Simultaneous Localization and Mapping (SLAM) to enable robot relocalization and loop closure detection. In topological SLAM the recognition takes place by comparing a signature (or feature vector) associated to the current node with the signatures of the nodes in the known map. However, as the number of nodes increases, matching the current node signature against all the existing ones becomes inefficient and thwarts real-time navigation. In this paper we propose a novel approach to pre-select a subset of map nodes for place recognition. The map nodes are clustered during exploration and each cluster is associated with a region. The region labels become the prediction targets of a deep neural network and, during navigation, only the nodes associated with the regions predicted with high probability are considered for matching. While the proposed technique can be integrated in different SLAM approaches, in this work we describe an effective integration with RTAB-Map (a popular framework for real-time topological SLAM) which allowed us to design and run several experiments to demonstrate its effectiveness. All the code and material from the experiments will be available online at https://github.com/MI-BioLab/region-learner.

翻译：识别已探索场所（即位置识别）是同步定位与地图构建（SLAM）中的基本任务，用于实现机器人的重定位与闭环检测。在拓扑SLAM中，位置识别通过将当前节点对应的特征描述子（或特征向量）与已知地图中的节点描述子进行比对来实现。然而，随着节点数量增加，将当前节点描述子与所有现有节点逐一匹配会降低效率并阻碍实时导航。本文提出一种新颖的方法，用于预选地图节点子集进行位置识别。探索过程中对地图节点进行聚类，每个聚类关联一个区域。区域标签成为深度神经网络的预测目标，在导航过程中，仅考虑与高概率预测区域相关联的节点进行匹配。所提技术可集成至不同SLAM方法中，本文详细描述了其与RTAB-Map（一种流行的实时拓扑SLAM框架）的有效集成方案，并据此设计开展了多项实验以验证其有效性。所有实验代码与资料将在 https://github.com/MI-BioLab/region-learner 公开提供。