  Pole-like landmark has received increasing attention as a domain-invariant
visual cue for visual robot self-localization across domains (e.g., seasons,
times of day, weathers). However, self-localization using pole-like landmarks
can be ill-posed for a passive observer, as many viewpoints may not provide any
pole-like landmark view. To alleviate this problem, we consider an active
observer and explore a novel "domain-invariant" next-best-view (NBV) planner
that attains consistent performance over different domains (i.e.,
maintenance-free), without requiring the expensive task of training data
collection and retraining. In our approach, a novel multi-encoder deep
convolutional neural network enables to detect domain invariant pole-like
landmarks, which are then used as the sole input to a model-free deep
reinforcement learning -based domain-invariant NBV planner. Further, we develop
a practical system for active self-localization using sparse invariant
landmarks and dense discriminative landmarks. In experiments, we demonstrate
that the proposed method is effective both in efficient landmark detection and
in discriminative self-localization.
