Deployment-Ready UWB Localization for Industrial Ground Robots with Automatic Anchor Calibration and Terrain-Aware Fusion

2026-07-17

AI Summary

This work addresses the challenges of cumbersome anchor calibration and inconsistent multi-sensor fusion in ultra-wideband (UWB)-based positioning for industrial autonomous mobile robots (AMRs). The authors propose an end-to-end localization framework that first introduces an automatic, human-intervention-free anchor calibration method and then develops a bias-aware, terrain-adaptive multi-sensor fusion estimator. By jointly optimizing anchor positions and UWB ranging biases within an extended Kalman filter formulation, the approach supports sparse anchor deployments and non-line-of-sight scenarios. Experimental validation on commercial logistics AMR and forklift datasets demonstrates a significant reduction in deployment costs while achieving high-precision, consistent industrial-grade localization performance in both indoor and outdoor environments. The study also contributes a publicly released warehouse environment dataset.

Abstract

Ultra-Wideband (UWB) ranging has become a viable option for industrial Autonomous Mobile Robot (AMR) localization due to improved accuracy and low cost. However, real-world deployments remain limited by two recurring challenges: calibrating static anchors can be time-consuming and error-prone, and integrating UWB with existing onboard sensors requires careful design to ensure robust and consistent pose estimation. Addressing these challenges, this paper presents an end-to-end pipeline that combines automatic anchor calibration with a generic multi-sensor estimator tailored to surface-bound vehicle motion. It targets existing AMR stacks in scenarios where robot pose priors are available for initialization. The calibration stage estimates anchor positions and range biases, while the localization stage fuses UWB with proprioceptive sensing in a bias-aware Extended Kalman Filter to improve consistency without extensive parameter tuning. Experiments on a commercial logistics AMR in a warehouse setting demonstrate accurate positioning indoors and across outdoor transitions, with improved consistency compared to an earlier estimator formulation. Evaluation on an independent forklift dataset further indicates transferability to other platforms. The method remains effective in test cases with limited line-of-sight and sparse anchor coverage. These results show that UWB localization can be deployed with substantially reduced manual effort while preserving the accuracy required for industrial AMRs. The collected warehouse dataset is made publicly available.

Problem

• UWB localization
• anchor calibration
• multi-sensor fusion
• industrial AMR
• pose estimation

Innovation

• automatic anchor calibration
• terrain-aware fusion
• bias-aware EKF
• UWB localization
• multi-sensor estimation