POMP++: Pomcp-based Active Visual Search in unknown indoor environments release_u2edvnoapfgppcewu7xc5fi3cu

by Francesco Giuliari, Alberto Castellini, Riccardo Berra, Alessio Del Bue, Alessandro Farinelli, Marco Cristani, Francesco Setti, Yiming Wang

Released as a article .

2021  

Abstract

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.
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Date   2021-11-05
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arXiv  2107.00914v2
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