PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model

TL;DR

PALMS+ is a modular indoor localization system that reconstructs 3D scenes from RGB images using depth estimation, enabling accurate, infrastructure-free positioning in GPS-denied environments without training.

Contribution

It introduces PALMS+ which leverages foundation monocular depth models for scalable, accurate indoor localization from RGB images, outperforming prior methods without training.

Findings

Outperforms PALMS and F3Loc in stationary localization accuracy

Achieves lower localization errors in sequential tracking

Demonstrates robustness in real-world indoor environments

Abstract

Indoor localization in GPS-denied environments is crucial for applications like emergency response and assistive navigation. Vision-based methods such as PALMS enable infrastructure-free localization using only a floor plan and a stationary scan, but are limited by the short range of smartphone LiDAR and ambiguity in indoor layouts. We propose PALMS$+$, a modular, image-based system that addresses these challenges by reconstructing scale-aligned 3D point clouds from posed RGB images using a foundation monocular depth estimation model (Depth Pro), followed by geometric layout matching via convolution with the floor plan. PALMS$+$ outputs a posterior over the location and orientation, usable for direct or sequential localization. Evaluated on the Structured3D and a custom campus dataset consisting of 80 observations across four large campus buildings, PALMS$+$ outperforms PALMS and F3Loc in stationary localization accuracy -- without requiring any training. Furthermore, when integrated with a particle filter for sequential localization on 33 real-world trajectories, PALMS$+$ achieved lower localization errors compared to other methods, demonstrating robustness for camera-free tracking and its potential for infrastructure-free applications. Code and data are available at https://github.com/Head-inthe-Cloud/PALMS-Plane-based-Accessible-Indoor-Localization-Using-Mobile-Smartphones