Large-Scale UWB Anchor Calibration and One-Shot Localization Using Gaussian Process
Abstract
Ultra-wideband (UWB) is gaining popularity with devices like AirTags for precise home item localization but faces significant challenges when scaled to large environments like seaports. The main challenges are calibration and localization in obstructed conditions, which are common in logistics environments. Traditional calibration methods, dependent on line-of-sight (LoS), are slow, costly, and unreliable in seaports and warehouses, making large-scale localization a significant pain point in the industry. To overcome these challenges, we propose a UWB-LiDAR fusion-based calibration and one-shot localization framework. Our method uses Gaussian Processes to estimate anchor position from continuous-time LiDAR Inertial Odometry with sampled UWB ranges. This approach ensures accurate and reliable calibration with just one round of sampling in large-scale areas, I.e., 600x450 square meter. With the LoS issues, UWB-only localization can be problematic, even when anchor positions are known. We demonstrate that by applying a UWB-range filter, the search range for LiDAR loop closure descriptors is significantly reduced, improving both accuracy and speed. This concept can be applied to other loop closure detection methods, enabling cost-effective localization in large-scale warehouses and seaports. It significantly improves precision in challenging environments where UWB-only and LiDAR-Inertial methods fall short, as shown in the video (https://youtu.be/oY8jQKdM7lU). We will open-source our datasets and calibration codes for community use.
Cite
@article{arxiv.2412.16880,
title = {Large-Scale UWB Anchor Calibration and One-Shot Localization Using Gaussian Process},
author = {Shenghai Yuan and Boyang Lou and Thien-Minh Nguyen and Pengyu Yin and Muqing Cao and Xinghang Xu and Jianping Li and Jie Xu and Siyu Chen and Lihua Xie},
journal= {arXiv preprint arXiv:2412.16880},
year = {2025}
}
Comments
This work has been accepted to IEEE International Conference on Robotics and Automation (ICRA) @ 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/redistribution, creating new works, or reuse of any copyrighted components of this work in other media