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.

翻译：本文提出UVIO，一种利用超宽带（UWB）技术与视觉惯性里程计（VIO）的多传感器框架，实现鲁棒且低漂移的定位。为了在状态估计中引入距离测量值，必须已知UWB锚点的位置。本研究提出一种多步初始化流程，通过无人机（UAV）以全自主方式对多个未知锚点进行定位。为解决通过随机轨迹初始化UWB锚点的局限性，本文采用几何精度因子（GDOP）作为锚点位置估计的最优性度量，计算一组最优航路点并合成一条可最小化定位不确定性的轨迹。初始化完成后，来自多个锚点的距离测量值（包括测量偏差）被紧密集成至VIO系统。在已初始化锚点覆盖范围内，VIO在位置和航向方向上的漂移被消除。通过一系列仿真与真实世界实验验证了UVIO及其初始化流程的有效性。