  The training of a next-best-view (NBV) planner for visual place recognition
(VPR) is a fundamentally important task in autonomous robot navigation, for
which a typical approach is the use of visual experiences that are collected in
the target domain as training data. However, the collection of a wide variety
of visual experiences in everyday navigation is costly and prohibitive for
real-time robotic applications. We address this issue by employing a novel {\it
domain-invariant} NBV planner. A standard VPR subsystem based on a
convolutional neural network (CNN) is assumed to be available, and its
domain-invariant state recognition ability is proposed to be transferred to
train the domain-invariant NBV planner. Specifically, we divide the visual cues
that are available from the CNN model into two types: the output layer cue
(OLC) and intermediate layer cue (ILC). The OLC is available at the output
layer of the CNN model and aims to estimate the state of the robot (e.g., the
robot viewpoint) with respect to the world-centric view coordinate system. The
ILC is available within the middle layers of the CNN model as a high-level
description of the visual content (e.g., a saliency image) with respect to the
ego-centric view. In our framework, the ILC and OLC are mapped to a state
vector and subsequently used to train a multiview NBV planner via deep
reinforcement learning. Experiments using the public NCLT dataset validate the
effectiveness of the proposed method.
