MR-ULINS：一种具有多历元离群点剔除的紧耦合UWB-LiDAR-惯性估计器
MR-ULINS: A Tightly-Coupled UWB-LiDAR-Inertial Estimator with Multi-Epoch Outlier Rejection
- Wuhan University（武汉大学）
- Hubei Luojia Laboratory（湖北珞珈实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出紧耦合UWB-LiDAR-惯性估计器MR-ULINS，通过在线补偿系统性测距误差并利用LIO相对轨迹进行多历元离群点剔除，在复杂室内环境及退化场景下实现了约0.1m的高精度鲁棒定位。
AI中文摘要：
LiDAR-惯性里程计（LIO）与超宽带（UWB）已被结合在一起，以在GNSS拒止环境中实现无漂移定位。然而，UWB可能会受到系统性测距误差（如时钟漂移和天线相位中心偏移）以及非视距（NLOS）信号的影响，导致鲁棒性降低。在本研究中，我们提出了一种UWB-LiDAR-惯性估计器（MR-ULINS），它在多状态约束卡尔曼滤波器（MSCKF）框架内紧耦合地融合了UWB测距、LiDAR帧间和IMU测量数据。系统性测距误差被精确建模，以进行在线估计和补偿。此外，我们利用LIO的相对精度，提出了一种针对UWB NLOS的多历元离群点剔除算法。具体而言，采用LIO的相对轨迹来验证滑动窗口内所有测距测量的一致性。大量实验结果表明，MR-ULINS在存在严重NLOS干扰的复杂室内环境中实现了约0.1 m的定位精度。消融实验表明，在线估计和多历元离群点剔除能够有效提高定位精度。此外，MR-ULINS在LiDAR退化场景和基站稀疏的UWB挑战性条件下仍保持高精度和鲁棒性。
英文摘要：
The LiDAR-inertial odometry (LIO) and the ultra-wideband (UWB) have been integrated together to achieve driftless positioning in global navigation satellite system (GNSS)-denied environments. However, the UWB may be affected by systematic range errors (such as the clock drift and the antenna phase center offset) and non-line-of-sight (NLOS) signals, resulting in reduced robustness. In this study, we propose a UWB-LiDAR-inertial estimator (MR-ULINS) that tightly integrates the UWB range, LiDAR frame-to-frame, and IMU measurements within the multi-state constraint Kalman filter (MSCKF) framework. The systematic range errors are precisely modeled to be estimated and compensated online. Besides, we propose a multi-epoch outlier rejection algorithm for UWB NLOS by utilizing the relative accuracy of the LIO. Specifically, the relative trajectory of the LIO is employed to verify the consistency of all range measurements within the sliding window. Extensive experiment results demonstrate that MR-ULINS achieves a positioning accuracy of around 0.1 m in complex indoor environments with severe NLOS interference. Ablation experiments show that the online estimation and multi-epoch outlier rejection can effectively improve the positioning accuracy. Besides, MR-ULINS maintains high accuracy and robustness in LiDAR-degenerated scenes and UWB-challenging conditions with spare base stations.
