MILUV: A Multi-UAV Indoor Localization dataset with UWB and Vision

TL;DR

MILUV addresses indoor multi-UAV localization by providing a rich dataset that combines low-level UWB data (timestamps, CIR, RSSI, clock skew) with vision, IMU, height, and magnetometer measurements across three quadcopters. The dataset is ground-truth labeled by Vicon and augmented with a comprehensive development kit to benchmark VIO, EKF-based UWB fusion, and VIO-UWB fusion pipelines, using $SE(2)$/$SE(3)$ pose representations where appropriate. Key contributions include the release of low-level UWB data and antenna-delay calibration, distributed UWB-vision data for multi-robot scenarios, and ready-to-run benchmarks to spur CIR-based and multi-robot SLAM research. This dataset facilitates robust comparisons and accelerates progress in multi-UAV localization under LOS/NLOS and cluttered indoor conditions, with potential extensions to outdoor deployments.

Abstract

This paper introduces MILUV, a Multi-UAV Indoor Localization dataset with UWB and Vision measurements. This dataset comprises 217 minutes of flight time over 36 experiments using three quadcopters, collecting ultra-wideband (UWB) ranging data such as the raw timestamps and channel-impulse response data, vision data from a stereo camera and a bottom-facing monocular camera, inertial measurement unit data, height measurements from a laser rangefinder, magnetometer data, and ground-truth poses from a motion-capture system. The UWB data is collected from up to 12 transceivers affixed to mobile robots and static tripods in both line-of-sight and non-line-of-sight conditions. The UAVs fly at a maximum speed of 4.418 m/s in an indoor environment with visual fiducial markers as features. MILUV is versatile and can be used for a wide range of applications beyond localization, but the primary purpose of MILUV is for testing and validating multi-robot UWB- and vision-based localization algorithms. The dataset can be downloaded at https://doi.org/10.25452/figshare.plus.28386041.v1. A development kit is presented alongside the MILUV dataset, which includes benchmarking algorithms such as visual-inertial odometry, UWB-based localization using an extended Kalman filter, and classification of CIR data using machine learning approaches. The development kit can be found at https://github.com/decargroup/miluv, and is supplemented with a website available at https://decargroup.github.io/miluv/.

Figures (15)

• Figure 1: The sensors equipped on the Uvify IFO-S quadcopter.
• Figure 2: The environmental setup, with (top) and without (bottom) obstacles.
• Figure 3: Sensor frames and their relative transforms.
• Figure 4: The three anchor constellations used in the data collection of MILUV.
• Figure 5: Positions of AprilTags used in the collection of this dataset.