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

TL;DR

UVIO is a multi-sensor framework combining UWB and VIO with an autonomous multi-step initialization to accurately map unknown anchors, significantly reducing localization drift in UAV navigation.

Contribution

The paper presents a novel autonomous initialization method for UWB anchors using GDOP optimization, integrated with VIO for robust UAV localization.

Findings

The proposed initialization reduces anchor mapping uncertainty.

UVIO achieves low-drift localization in simulations and real-world tests.

Range measurements with biases are effectively integrated into VIO.

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.