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.

翻译：超宽带（UWB）技术正随着AirTag等设备在家庭物品精确定位中日益普及，但在扩展到海港等大规模环境时面临重大挑战。主要挑战在于物流环境中常见的遮挡条件下的标定与定位问题。传统的标定方法依赖视距（LoS）条件，在海港和仓库中速度慢、成本高且不可靠，使得大规模定位成为行业痛点。为克服这些挑战，我们提出了一种基于UWB-LiDAR融合的标定与一发定位框架。该方法利用高斯过程，通过采样UWB测距数据从连续时间LiDAR惯性里程计中估计锚点位置。此方法仅需单轮采样即可在大规模区域（即600×450平方米）实现准确可靠的标定。即使已知锚点位置，仅依赖UWB的定位在存在视距问题时仍可能失效。我们证明，通过应用UWB测距滤波器，LiDAR闭环描述符的搜索范围可显著缩小，从而同时提升精度与速度。该理念可推广至其他闭环检测方法，实现大规模仓库与海港中经济高效的定位。如演示视频（https://youtu.be/oY8jQKdM7lU）所示，本方法在纯UWB与LiDAR-惯性方法表现不佳的复杂环境中显著提升了定位精度。我们将开源数据集与标定代码以供社区使用。