NLOS Identification- and Correction-Focused Fusion of UWB and LiDAR-SLAM Based on Factor Graph Optimization for High-Precision Positioning with Reduced Drift

Abstract

In this study, we propose a tightly coupled integrated method of ultrawideband (UWB) and light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) for global navigation satellite system (GNSS)-denied environments to achieve high-precision positioning with reduced drift. Specifically, we focus on non-line-of-sight (NLOS) identification and correction. In previous work, we utilized laser point cloud maps to identify and exclude NLOS measurements in real time to attenuate their severe effects on the integrated system. However, the complete exclusion of NLOS measurements will likely lead to deterioration in the dilution of precision (DOP) for the remaining line-of-sight (LOS) anchors, counterproductively introducing large positioning errors into the integrated system. Therefore, this study considers the ranging accuracy and geometric distribution of UWB anchors and innovatively proposes an NLOS correction method using a grey prediction model. For a poor line-of-sight (LOS) anchor geometric distribution, the grey prediction model is used to fill in the gaps by predicting the NLOS measurements based on historical measurements. Including the corrected measurements effectively improves the original poor geometric configuration, improving the system positioning accuracy. Since conventional filtering-based fusion methods are exceedingly sensitive to measurement outliers, we use state-of-the-art factor graph optimization (FGO) to tightly integrate the UWB measurements (LOS and corrected measurements) with LiDAR-SLAM. The temporal correlation between measurements and the redundant system measurements effectively enhance the robustness of the integrated system. Experimental results show that the tightly coupled integrated method combining NLOS correction and FGO improves the positioning accuracy under a poor geometric distribution, increases the system availability, and achieves better positioning than filtering-based fusion methods with a root-mean-square error of 0.086 m in the plane direction, achieving subdecimeter indoor high-precision positioning.

Multi-Scenario Variable-State Robust Fusion Algorithm for Ranging Analysis Framework

“…Through the fusion of GNSS/UWB/Inertial Navigation System (INS) [39], it provides the motion attitude characteristics of the farm machinery body to solve the problem of packet loss in the case of NLOS of GNSS signals. Furthermore, the simultaneous localization and mapping (SLAM) technique [40], real-time dynamic acquisition of environmental features, is used to assist GNSS positioning. Inspired by this, in future research, the research of GNSS/UWB/INS/SLAM multi-source information fusion positioning technology based on the extended Kalman filter algorithm will be considered to improve the positioning accuracy of agricultural machines in different environments.…”

A Dynamic UKF-Based UWB/Wheel Odometry Tightly Coupled Approach for Indoor Positioning

“…The final category involves utilising other sensors to identify and mitigate NLOS effects. Previous researchers have conducted extensive studies on integrating IMU [24,25], cameraa [26], and LiDAR [27,28] with UWB technology for indoor positioning systems. Those studies have some drawbacks.…”

A Novel Loosely Coupling Fusion Approach of Ultra-Wideband and Wheel Odometry for Indoor Localisation

“…Through multi-sensor fusion, the system not only aids UWB technology in identifying and mitigating NLOS errors to enhance positioning precision but also augments the system's resilience in demanding environmental conditions. Commonly integrated sensors with UWB encompass IMU [22,23], Visual [24], odometers [25], and LiDAR [26]. Among these, wheeled odometers have historically received less attention from researchers, primarily due to their constrained applicability, typically limited to two-dimensional vehicular movement in scenarios involving wheeled AGVs.…”