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Nicholas Dahdah

nicholas.dahdah@mila.quebec
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e
James Richard Forbes
Sujets de recherche
Navigation robotique autonome
Optimisation
Robotique
Traitement du signal
Site web
Google Scholar
GitHub

Publications

MILUV: A Multi-UAV Indoor Localization dataset with UWB and Vision
Mohammed Ayman Shalaby
Nicholas Dahdah
Syed Shabbir Ahmed
Charles Champagne Cossette
Jerome Le Ny
James Richard Forbes
This paper introduces MILUV, a Multi-UAV Indoor Localization dataset with UWB and Vision measurements. This dataset comprises 217 minutes of… (voir plus) 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/.
2026-01-08
International Journal of Robotics Research (publié)
doi.org
arxiv.org