# Robust Onboard Localization in Changing Environments Exploiting Text Spotting

arXiv: https://arxiv.org/abs/2203.12647 · HTML: https://mlsanity.com/paper/2203.12647/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2022.

## Built on by (most cited first, 6 of 6)

- [IR-MCL: Implicit Representation-Based Online Global Localization](https://mlsanity.com/paper/2210.03113/) (2022)
- [Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments](https://mlsanity.com/paper/2208.07364/) (2022)
- [PALMS: Plane-based Accessible Indoor Localization Using Mobile Smartphones](https://mlsanity.com/paper/2410.15694/) (2024)
- [ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors](https://mlsanity.com/paper/2512.09065/) (2025)
- [Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization](https://mlsanity.com/paper/2303.10959/) (2023)
- [VLM-GLoc: Vision-Language Model Enhanced Monte Carlo Localization for Robust Semantic Global Localization in Cluttered Quasi-Static Environments](https://mlsanity.com/paper/2605.30506/) (2026)

## Abstract (arXiv)

Robust localization in a given map is a crucial component of most autonomous robots. In this paper, we address the problem of localizing in an indoor environment that changes and where prominent structures have no correspondence in the map built at a different point in time. To overcome the discrepancy between the map and the observed environment caused by such changes, we exploit human-readable localization cues to assist localization. These cues are readily available in most facilities and can be detected using RGB camera images by utilizing text spotting. We integrate these cues into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. By this, we provide a robust localization solution for environments with structural changes and dynamics by humans walking. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We release an open source implementation of our approach (upon paper acceptance), which uses off-the-shelf text spotting, written in C++ with a ROS wrapper.