Large-Scale UWB Anchor Calibration and One-Shot Localization Using Gaussian Process

2024-12-22

arXiv.org

AI Summary

In large-scale logistics environments (e.g., ports, warehouses), Ultra-Wideband (UWB) systems suffer from inaccurate anchor calibration under non-line-of-sight (NLoS) conditions and unreliable single-shot positioning. Method: This paper proposes a UWB–LiDAR tightly coupled Gaussian process calibration framework. It enables centimeter-level anchor calibration over a 600×450 m² area in a single pass; integrates UWB range filtering to assist LiDAR loop closure detection, enhancing robustness under occlusion; and jointly optimizes continuous-time LiDAR-inertial odometry with UWB distance constraints for loop closure refinement. Contribution/Results: The method achieves sub-meter single-shot positioning accuracy—significantly outperforming standalone UWB or LiDAR-inertial approaches. A benchmark dataset and open-source calibration code are released to support reproducibility and community advancement.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Calibration and localization in large-scale obstructed environments.
• Overcoming line-of-sight dependency in UWB anchor calibration.
• Improving accuracy and speed in large-scale logistics localization.

Innovation

• UWB-LiDAR fusion for large-scale calibration
• Gaussian Processes for anchor position estimation
• UWB-range filter enhances LiDAR loop closure