  Landmark-based robot self-localization has recently garnered interest as a
highly-compressive domain-invariant approach for performing visual place
recognition (VPR) across domains (e.g., time of day, weather, and season).
However, landmark-based self-localization can be an ill-posed problem for a
passive observer (e.g., manual robot control), as many viewpoints may not
provide an effective landmark view. In this study, we consider an active
self-localization task by an active observer and present a novel reinforcement
learning (RL)-based next-best-view (NBV) planner. Our contributions are as
follows. (1) SIMBAD-based VPR: We formulate the problem of landmark-based
compact scene description as SIMBAD (similarity-based pattern recognition) and
further present its deep learning extension. (2) VPR-to-NBV knowledge transfer:
We address the challenge of RL under uncertainty (i.e., active
self-localization) by transferring the state recognition ability of VPR to the
NBV. (3) NNQL-based NBV: We regard the available VPR as the experience database
by adapting nearest-neighbor approximation of Q-learning (NNQL). The result
shows an extremely compact data structure that compresses both the VPR and NBV
into a single incremental inverted index. Experiments using the public NCLT
dataset validated the effectiveness of the proposed approach.
