融合UWB与SLAM数据，实现无人机飞行中坐标一致的精准定位
Coordinate-Consistent Localization via Continuous-Time Calibration and Fusion of UWB and SLAM Observations
- 分两阶段：先用单次飞行数据校准锚点三维位置，再持续融合多轮数据
- 仅需一次校准，后续所有飞行任务均能实现厘米级坐标一致性定位
- 适合需要长期稳定定位的无人机、自动驾驶等场景
机载同时定位与地图构建（SLAM）常用于为自主机器人提供高精度定位信息，但其坐标原点在每次运行时会重置。而基于固定锚点的超宽带（UWB）定位可保证跨会话坐标一致性，但需准确已知锚点坐标。为此，本文提出一种两阶段方法：第一阶段通过连续时间批量优化，利用单次完整飞行中的测距和里程计数据，结合高度先验与锚点间距离因子，恢复锚点三维位置；第二阶段采用滑动窗口优化，融合后续飞行中的UWB与SLAM数据，实现同一坐标系下的精准定位。在NTU VIRAL数据集的六种无人机飞行场景中验证，仅需一次校准即可使后续所有飞行任务实现高精度定位。源代码已开源：https://github.com/ntdathp/slam-uwb-calibration。
原文摘要 · Abstract (English)
Onboard simultaneous localization and mapping (SLAM) methods are commonly used to provide accurate localization information for autonomous robots. However, the coordinate origin of SLAM estimate often resets for each run. On the other hand, UWB-based localization with fixed anchors can ensure a consistent coordinate reference across sessions; however, it requires an accurate assignment of the anchor nodes' coordinates. To this end, we propose a two-stage approach that calibrates and fuses UWB data and SLAM data to achieve coordinate-wise consistent and accurate localization in the same environment. In the first stage, we solve a continuous-time batch optimization problem by using the range and odometry data from one full run, incorporating height priors and anchor-to-anchor distance factors to recover the anchors' 3D positions. For the subsequent runs in the second stage, a sliding-window optimization scheme fuses the UWB and SLAM data, which facilitates accurate localization in the same coordinate system. Experiments are carried out on the NTU VIRAL dataset with six scenarios of UAV flight, and we show that calibration using data in one run is sufficient to enable accurate localization in the remaining runs. We release our source code to benefit the community at https://github.com/ntdathp/slam-uwb-calibration.
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