基于UWB与SLAM观测的连续时间标定与融合的坐标一致定位
Coordinate-Consistent Localization via Continuous-Time Calibration and Fusion of UWB and SLAM Observations
AI总结：
提出两阶段方法，通过连续时间标定UWB锚点坐标并融合SLAM数据，实现跨运行坐标一致的准确定位，实验验证单次标定即可支持后续运行。
摘要：
机载同步定位与建图（SLAM）方法通常用于为自主机器人提供准确的定位信息。然而，SLAM估计的坐标原点在每次运行时常常会重置。另一方面，基于UWB的定位使用固定锚点可以确保跨会话的坐标参考一致；然而，它需要准确分配锚节点的坐标。为此，我们提出了一种两阶段方法，标定并融合UWB数据和SLAM数据，以在同一环境中实现坐标一致且准确的定位。在第一阶段，我们利用一次完整运行中的距离和里程计数据求解一个连续时间批量优化问题，结合高度先验和锚点间距离因子，以恢复锚点的三维位置。在第二阶段的后续运行中，采用滑动窗口优化方案融合UWB和SLAM数据，从而在同一坐标系中实现准确定位。我们在NTU VIRAL数据集上进行了实验，包含六个无人机飞行场景，并表明使用一次运行的数据进行标定足以在其余运行中实现准确定位。我们发布了源代码以惠及社区，网址为https://github.com/ntdathp/slam-uwb-calibration。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
