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

arXiv: https://arxiv.org/abs/2107.00914 · HTML: https://mlsanity.com/paper/2107.00914/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2021.

## Built on by (most cited first, 4 of 4)

- [Navigating to Objects in Unseen Environments by Distance Prediction](https://mlsanity.com/paper/2202.03735/) (2022)
- [Spatial Commonsense Graph for Object Localisation in Partial Scenes](https://mlsanity.com/paper/2203.05380/) (2022)
- [A System for Generalized 3D Multi-Object Search](https://mlsanity.com/paper/2303.03178/) (2023)
- [Learning Logic Specifications for Soft Policy Guidance in POMCP](https://mlsanity.com/paper/2303.09172/) (2023)

## Abstract (arXiv)

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.