# PALMS: Plane-based Accessible Indoor Localization Using Mobile Smartphones

arXiv: https://arxiv.org/abs/2410.15694 · HTML: https://mlsanity.com/paper/2410.15694/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments](https://mlsanity.com/paper/2104.09169/) (2021)
- [SeDAR - Semantic Detection and Ranging: Humans can localise without LiDAR, can robots?](https://mlsanity.com/paper/1709.01500/) (2017)
- [Robust Onboard Localization in Changing Environments Exploiting Text Spotting](https://mlsanity.com/paper/2203.12647/) (2022)
- [FP-Loc: Lightweight and Drift-free Floor Plan-assisted LiDAR Localization](https://mlsanity.com/paper/2203.00292/) (2022)
- [All the Way There and Back: Inertial-Based, Phone-in-Pocket Indoor Wayfinding and Backtracking Apps for Blind Travelers](https://mlsanity.com/paper/2401.08021/) (2024)

## Built on by (most cited first, 2 of 2)

- [PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model](https://mlsanity.com/paper/2511.09724/) (2025)
- [SlugTrails: An Egocentric Benchmark for Floor Plan Localization in Large Buildings](https://mlsanity.com/paper/2609.19876/) (2026)

## Abstract (arXiv)

In this paper, we present PALMS, an innovative indoor global localization and relocalization system for mobile smartphones that utilizes publicly available floor plans. Unlike most vision-based methods that require constant visual input, our system adopts a dynamic form of localization that considers a single instantaneous observation and odometry data. The core contribution of this work is the introduction of a particle filter initialization method that leverages the Certainly Empty Space (CES) constraint along with principal orientation matching. This approach creates a spatial probability distribution of the device's location, significantly improving localization accuracy and reducing particle filter convergence time. Our experimental evaluations demonstrate that PALMS outperforms traditional methods with uniformly initialized particle filters, providing a more efficient and accessible approach to indoor wayfinding. By eliminating the need for prior environmental fingerprinting, PALMS provides a scalable and practical approach to indoor navigation.