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
this https URL.

本文提出了一种预选地图节点进行场所识别的新方法，并将其与 RTAB-Map 集成，证明了其在实时导航中的有效性。