Plug-and-Play Label Map Diffusion for Universal Goal-Oriented Navigation

TL;DR

This paper introduces PLMD, a diffusion-based model that completes obstacle and semantic maps for goal localization in partial environments, enhancing goal-oriented navigation.

Contribution

The paper presents a novel diffusion model for map completion that improves semantic consistency and enables accurate goal localization in partial maps.

Findings

PLMD effectively expands unknown map regions.

It integrates seamlessly with existing navigation strategies.

Achieves state-of-the-art results on three GON tasks.

Abstract

In embodied vision, Goal-Oriented Navigation (GON) requires robots to locate a specific goal within an unexplored environment. The primary challenge of GON arises from the need to construct a Bird's-Eye-View (BEV) map to understand the environment while simultaneously localizing an unobserved goal. Existing map-based methods typically employ self-centered semantic maps, often facing challenges such as reliance on complete maps or inconsistent semantic association. To this end, we propose Plug-and-Play Label Map Diffusion (PLMD), which defines a novel map completion diffusion model based on Denoising Diffusion Probabilistic Models (DDPM). PLMD generates obstacle and semantic labels for unobserved regions through a diffusion-based completion process, thereby enabling goal localization even in partially observed environments. Moreover, it mitigates inconsistent semantic association by leveraging structural consistency between known and unknown obstacle layouts and integrating obstacle priors into the semantic denoising process. By substituting predicted labels for unobserved regions, robots can accurately localize the specified objects. Extensive experiments demonstrate that PLMD \textbf{(I)} effectively expands the region of unknown maps, \textbf{(II)} integrates seamlessly into existing navigation strategies that rely on semantic maps, \textbf{(III)} achieves state-of-the-art performance on three GON tasks.