Recent advances in 3D scene reconstruction, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting, have demonstrated remarkable results in novel view synthesis and dynamic scene representation. Despite these successes, existing approaches rely on time-synchronized multi-view imagery captured using specialized camera rigs in controlled environments. This reliance limits their applicability in uncontrolled, unbounded dynamic scenes. In this work, we propose a novel Unmanned Aerial Vehicle (UAV) based multi-view capture system that leverages GNSS Pulse Per Second (PPS) signals for precise frame synchronization across multiple cameras. Our system eliminates the need for fixed infrastructure, enabling flexible and scalable data collection for dynamic scene reconstruction in diverse environments. In addition to the system architecture, we also introduce a dataset of synchronized multi-view images captured in unbounded outdoor scenes from four synchronized UAVs, each carrying a stereo camera rig. We benchmark several 3D and 4D representation methods on our dataset and highlight the challenges associated with data collection in unstructured outdoor settings such as sparse views, varied lighting conditions, visual degradation etc. Our hardware configuration details, software details and dataset is available at https://github.com/neufieldrobotics/Dynamic_Mapping.

Wireless communication between an unmanned aerial vehicle (UAV) and the ground base station is susceptible to adversarial jamming. In such situations, it is important for the UAV to indicate a new channel to the BS. This paper describes a method of creating spatial codes that map the chosen channel to the location of the UAVs in space, wherein the latter physically traverses the space from a given so called ”constellation points” to another. These points create patterns in the sky, analogous to modulation constellations in classical wireless communications, and are detected at the BS through a millimeter-wave radar sensor. A constellation point represents a distinct n-bit field mapped to a specific channel, allowing simultaneous frequency switching at both ends without any RF transmissions. The main contributions of this paper are: (i) We conduct experimental studies to demonstrate how such constellations may be formed using COTS UAVs and mmWave sensors, (ii) We develop a theoretical framework that maps a desired constellation design to error and band switching time, including multi-user scenario-specific challenges, (iii) We compare our approach against current FHSS technology and (iv) We experimentally demonstrate jamming resilient communications and validate system goodput for links formed by UAV-mounted software defined radios.

Wireless communication between an unmanned aerial vehicle (UAV) and the ground base station (BS) is susceptible to adversarial jamming. In such situations, it is important for the UAV to indicate a new channel to the BS. This paper describes a method of creating spatial codes that map the chosen channel to the motion and location of the UAVs in space, wherein the latter physically traverses the space from a given so called ''constellation point'' to another. These points create patterns in the sky, analogous to modulation constellations in classical wireless communications, and are detected at the BS through a millimeter-wave (mmWave) radar sensor. A constellation point represents a distinct n-bit field mapped to a specific channel, allowing simultaneous frequency switching at both ends without any RF transmissions. The main contributions of this paper are: (i) We conduct experimental studies to demonstrate how such constellations may be formed using COTS UAVs and mmWave sensors, given realistic sensing errors and hovering vibrations, (ii) We develop a theoretical framework that maps a desired constellation design to error and band switching time, considering again practical UAV movement limitations, and (iii) We experimentally demonstrate jamming resilient communications and validate system goodput for links formed by UAV-mounted software defined radios.

We propose AirBeam, the first complete algorithmic framework and systems implementation of distributed air-to-ground beamforming on a fleet of UAVs. AirBeam synchronizes software defined radios (SDRs) mounted on each UAV and assigns beamforming weights to ensure high levels of directivity. We show through an exhaustive set of the experimental studies on UAVs why this problem is difficult given the continuous hovering-related fluctuations, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power and cost (SWaP-C) constraints for UAVs. AirBeam addresses these challenges through: (i) a channel state estimation method using Gold sequences that is used for setting the suitable beamforming weights, (ii) adaptively starting transmission to synchronize the action of the distributed radios, (iii) a channel state feedback process that exploits statistical knowledge of hovering characteristics. Finally, AirBeam provides insights from a systems integration viewpoint, with reconfigurable B210 SDRs mounted on a fleet of DJI M100 UAVs, using GnuRadio running on an embedded computing host.