MR-ULINS: A Tightly-Coupled UWB-LiDAR-Inertial Estimator with Multi-Epoch Outlier Rejection
Tisheng Zhang, Man Yuan, Linfu Wei, Yan Wang, Hailiang Tang, Xiaoji Niu

TL;DR
This paper introduces MR-ULINS, a tightly-coupled UWB-LiDAR-inertial estimator that enhances positioning accuracy and robustness in GNSS-denied environments by modeling errors and rejecting outliers using multi-epoch analysis.
Contribution
The paper presents a novel multi-epoch outlier rejection algorithm and online error compensation within a MSCKF framework for UWB-LiDAR-inertial navigation.
Findings
Achieves around 0.1 m accuracy in complex indoor environments.
Online error estimation improves robustness against NLOS signals.
Maintains high accuracy in LiDAR-degenerated and UWB-challenging scenarios.
Abstract
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…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Target Tracking and Data Fusion in Sensor Networks · Advanced SAR Imaging Techniques
