Perspective from a Broader Context: Can Room Style Knowledge Help Visual Floorplan Localization?

TL;DR

This paper introduces a novel approach for visual floorplan localization that leverages broader scene context and room style knowledge through unsupervised learning, significantly improving robustness and accuracy over existing methods.

Contribution

It proposes an unsupervised pre-training method using a room discriminator to incorporate scene context into FLoc algorithms, enhancing localization performance.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Achieves significant improvements in robustness and accuracy.

Effectively utilizes scene context and room style knowledge.

Abstract

Since a building's floorplan remains consistent over time and is inherently robust to changes in visual appearance, visual Floorplan Localization (FLoc) has received increasing attention from researchers. However, as a compact and minimalist representation of the building's layout, floorplans contain many repetitive structures (e.g., hallways and corners), thus easily result in ambiguous localization. Existing methods either pin their hopes on matching 2D structural cues in floorplans or rely on 3D geometry-constrained visual pre-trainings, ignoring the richer contextual information provided by visual images. In this paper, we suggest using broader visual scene context to empower FLoc algorithms with scene layout priors to eliminate localization uncertainty. In particular, we propose an unsupervised learning technique with clustering constraints to pre-train a room discriminator on self-collected unlabeled room images. Such a discriminator can empirically extract the hidden room type of the observed image and distinguish it from other room types. By injecting the scene context information summarized by the discriminator into an FLoc algorithm, the room style knowledge is effectively exploited to guide definite visual FLoc. We conducted sufficient comparative studies on two standard visual Floc benchmarks. Our experiments show that our approach outperforms state-of-the-art methods and achieves significant improvements in robustness and accuracy.