  In this paper we focus on the problem of learning online an optimal policy
for Active Visual Search (AVS) of objects in unknown indoor environments. We
propose POMP++, a planning strategy that introduces a novel formulation on top
of the classic Partially Observable Monte Carlo Planning (POMCP) framework, to
allow training-free online policy learning in unknown environments. We present
a new belief reinvigoration strategy which allows to use POMCP with a
dynamically growing state space to address the online generation of the floor
map. We evaluate our method on two public benchmark datasets, AVD that is
acquired by real robotic platforms and Habitat ObjectNav that is rendered from
real 3D scene scans, achieving the best success rate with an improvement of
>10% over the state-of-the-art methods.
