Robust simultaneous UWB-anchor calibration and robot localization for emergency situations

2025-03-28

AI Summary

In emergency scenarios, manual UWB anchor calibration is time-consuming and often infeasible, while robot localization suffers from insufficient robustness. To address these challenges, this paper proposes a tightly coupled multi-sensor simultaneous calibration and localization framework based on factor graph optimization (FGO). The method fuses onboard UWB range measurements and LiDAR SLAM data to jointly estimate both anchor positions and robot trajectories. Crucially, it introduces the novel concept of a “soft sensor” model for UWB anchors—treating anchor locations as latent variables within the factor graph—enabling joint inference without prior anchor knowledge. The framework supports fully automatic, synchronous calibration of scalable, large-scale UWB networks and achieves high-precision 3D localization. In experiments, it completes calibration of four anchors and full robot pose estimation within 30 seconds, significantly reducing human intervention, deployment error, and dependency on initial guesses—thereby enhancing system autonomy, robustness, and localization accuracy.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this work, we propose a factor graph optimization (FGO) framework to simultaneously solve the calibration problem for Ultra-WideBand (UWB) anchors and the robot localization problem. Calibrating UWB anchors manually can be time-consuming and even impossible in emergencies or those situations without special calibration tools. Therefore, automatic estimation of the anchor positions becomes a necessity. The proposed method enables the creation of a soft sensor providing the position information of the anchors in a UWB network. This soft sensor requires only UWB and LiDAR measurements measured from a moving robot. The proposed FGO framework is suitable for the calibration of an extendable large UWB network. Moreover, the anchor calibration problem and robot localization problem can be solved simultaneously, which saves time for UWB network deployment. The proposed framework also helps to avoid artificial errors in the UWB-anchor position estimation and improves the accuracy and robustness of the robot-pose. The experimental results of the robot localization using LiDAR and a UWB network in a 3D environment are discussed, demonstrating the performance of the proposed method. More specifically, the anchor calibration problem with four anchors and the robot localization problem can be solved simultaneously and automatically within 30 seconds by the proposed framework. The supplementary video and codes can be accessed via https://github.com/LiuxhRobotAI/Simultaneous_calibration_localization.

Problem

• Simultaneously calibrate UWB anchors and localize robots automatically.
• Enable anchor position estimation without manual calibration tools.
• Improve accuracy and robustness in emergency UWB network deployment.

Innovation

• Factor graph optimization for UWB calibration
• Simultaneous anchor and robot localization
• UWB and LiDAR measurements integration