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UVIO: An UWB-Aided Visual-Inertial Odometry Framework with Bias-Compensated Anchors Initialization

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abstract

This paper introduces UVIO, a multi-sensor framework that leverages Ultra Wide Band (UWB) technology and Visual-Inertial Odometry (VIO) to provide robust and low-drift localization. In order to include range measurements in state estimation, the position of the UWB anchors must be known. This study proposes a multi-step initialization procedure to map multiple unknown anchors by an Unmanned Aerial Vehicle (UAV), in a fully autonomous fashion. To address the limitations of initializing UWB anchors via a random trajectory, this paper uses the Geometric Dilution of Precision (GDOP) as a measure of optimality in anchor position estimation, to compute a set of optimal waypoints and synthesize a trajectory that minimizes the mapping uncertainty. After the initialization is complete, the range measurements from multiple anchors, including measurement biases, are tightly integrated into the VIO system. While in range of the initialized anchors, the VIO drift in position and heading is eliminated. The effectiveness of UVIO and our initialization procedure has been validated through a series of simulations and real-world experiments.

fields

cs.IT 1

years

2025 1

verdicts

UNVERDICTED 1

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  • A Robust 5G Terrestrial Positioning System with Sensor Fusion in GNSS-denied Scenarios cs.IT · 2025-07-22 · unverdicted · none · ref 35 · internal anchor

    The paper presents a 5G terrestrial positioning system using multi-carrier carrier phase ranging, deep learning for NLOS identification, and IMU/camera sensor fusion via error-state EKF, achieving less than 5 meters error on simulated 5G signals over KITTI urban trajectories.