  In ground-view object change detection, the recently emerging mapless
navigation has great potential to navigate a robot to objects distantly
detected (e.g., books, cups, clothes) and acquire high-resolution object
images, to identify their change states (no-change/appear/disappear). However,
naively performing full journeys for every distant object requires huge
sense/plan/action costs, proportional to the number of objects and the
robot-to-object distance. To address this issue, we explore a new map-based
active vision problem in this work: ``Which journey should the robot select
next?" However, the feasibility of the active vision framework remains unclear;
Since distant objects are only uncertainly recognized, it is unclear whether
they can provide sufficient cues for action planning. This work presents an
efficient simulator for feasibility testing, to accelerate the early-stage R&D
cycles (e.g., prototyping, training, testing, and evaluation). The proposed
simulator is designed to identify the degree of difficulty that a robot vision
system (sensors/recognizers/planners/actuators) would face when applied to a
given environment (workspace/objects). Notably, it requires only one real-world
journey experience per distant object to function, making it suitable for an
efficient R&D cycle. Another contribution of this work is to present a new
lightweight planner inspired by the traditional multi-armed bandit problem.
Specifically, we build a lightweight map-based planner on top of the mapless
planner, which constitutes a hierarchical action planner. We verified the
effectiveness of the proposed framework using a semantically non-trivial
scenario ``sofa as bookshelf".
