PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model
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
Cite
@article{arxiv.2511.09724,
title = {PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model},
author = {Yunqian Cheng and Benjamin Princen and Roberto Manduchi},
journal= {arXiv preprint arXiv:2511.09724},
year = {2025}
}
Comments
Accepted to IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026, Application Track. Main paper: 8 pages, 5 figures. Supplementary material included