UVIO: An UWB-Aided Visual-Inertial Odometry Framework with Bias-Compensated Anchors Initialization
This addresses localization challenges for unmanned aerial vehicles (UAVs) in environments where GPS is unreliable, though it appears incremental as it builds on existing VIO and UWB integration methods.
The paper tackles the problem of robust and low-drift localization by proposing UVIO, a framework that integrates Ultra Wide Band (UWB) with Visual-Inertial Odometry (VIO), using a multi-step initialization procedure to autonomously map UWB anchors and tightly integrate range measurements, resulting in elimination of VIO drift in position and heading when within range.
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.