This repository contains the implementation of the following publication:
@article{zimmerman2025ral,
title={SignLoc: Robust Localization using Navigation Signs and Public Maps},
author={Zimmerman, Nicky and Loo, Joel and Agrawal, Ayush and Hsu, David},
journal={IEEE Robotics and Automation Letters},
year={2025},
publisher={IEEE}
}Our live demo for both localization on the Spot platform can seen in the following video:
Navigation signs and maps, such as floor plans and street maps, are widely available and serve as ubiquitous aids for way-finding in human environments. Yet, they are rarely used by robot systems. This paper presents SignLoc, a global localization method that leverages navigation signs to localize the robot on publicly available maps—specifically floor plans and OpenStreetMap (OSM) graphs–without prior sensor-based mapping. SignLoc first extracts a navigation graph from the input map. It then employs a probabilistic observation model to match directional and locational cues from the detected signs to the graph, enabling robust topo-semantic localization within a Monte Carlo framework. We evaluated SignLoc in diverse large-scale environments: part of a university campus, a shopping mall, and a hospital complex. Experimental results show that SignLoc reliably localizes the robot after observing only one to two signs.
We provide both ROS Noetic and ROS Humble dockers for the installation
For ROS Noetic, run
roslaunch signloc_ros mclcpp.launch