A study has introduced an advanced method for visual-inertial navigation, utilizing a single Ultra-Wideband (UWB) anchor with an unknown position to achieve exceptional positioning accuracy and robustness in environments lacking Global Navigation Satellite System (GNSS) signals. This innovative approach effectively tackles long-standing challenges in multisensor fusion navigation, particularly the inaccuracies in UWB anchor position estimation and the improper weighting of heterogeneous sensors, significantly enhancing the performance of Visual-Inertial-UWB (VIU) systems.
In contemporary navigation systems, Global Navigation Satellite System (GNSS) technology is indispensable but encounters significant limitations in environments such as tunnels, underground spaces, and densely built urban areas due to signal shielding, multipath effects, and electromagnetic interference. These constraints have driven the evolution of multisource sensor-fusion navigation techniques. However, persistent issues like the inaccurate estimation of Ultra-Wideband (UWB) anchor positions and flawed sensor weighting continue to undermine the positioning precision and reliability of Visual-Inertial-UWB (VIU) systems. Addressing these critical issues necessitates focused research on refining UWB anchor position estimation and optimizing sensor weighting strategies.
On January 8, 2025, a research team a research team from the School of Geospatial Information at Information Engineering University, in collaboration with the State Key Laboratory of Geo-Information Engineering and the Shanghai Key Laboratory of Navigation and Location Based Services at Shanghai Jiao Tong University, unveiled their latest findings (DOI: 10.1186/s43020-024-00153-6) in the prestigious journal With increasing reliance on GPS technology, understanding and mitigating the effects of equatorial Satellite Navigation . Their study introduces a novel method to accurately estimate the position of a single UWB anchor with unknown coordinates, significantly enhancing VIU system performance in GNSS-denied environments. This breakthrough presents a promising solution for robust and precise navigation in challenging conditions.
The research proposes a comprehensive strategy to bolster VIU systems, employing a robust ridge nonlinear least-squares method to refine UWB anchor position estimations, thus mitigating cumulative errors and ensuring precise range measurements. A key innovation of the study is the introduction of a method grounded in the Geometric Dilution of Precision (GDOP) principle, enabling swift and accurate estimation of UWB anchor positions by treating various positions of a moving UWB tag as virtual anchors. Furthermore, the team developed a dynamically adaptive weighting approach based on the Helmert Variance Component Estimation (HVCE) principle, which assigns real-time data-driven weights to different sensors, significantly improving localization accuracy and system robustness. To validate these methods, the researchers designed a practical evaluation technique for estimating UWB anchor position errors in real-world environments. Extensive simulations and real-world experiments confirmed the method's superior performance over existing open-source systems like VINS-MONO and VIR-SLAM, marking a pivotal advancement in multisensor fusion navigation.
Guangyun Li, the study's corresponding author, emphasized the significance of their work: "Our method not only stabilizes UWB anchor position estimation but also offers a straightforward and effective means to evaluate positional accuracy. This represents a major leap forward in the field of multisensor fusion navigation."
The implications of this research are poised to revolutionize navigation in GNSS-challenged environments such as indoor spaces, urban canyons, and subterranean settings. The enhanced accuracy and resilience of the VIU system hold immense potential for advancing technologies in robotics, autonomous vehicles, and other sectors where precise positioning is critical. Looking to the future, the research team aims to integrate additional sensor data, including GNSS and LIDAR, to further elevate system capabilities. This visionary approach aspires to create a seamless, plug-and-play navigation solution that bridges indoor and outdoor environments, promising to significantly improve autonomy and reliability across diverse applications.
