Mr. Alfred Mayalu Profile

Alfred Mayalu

at Virginia Polytechnic Institute and State Univ

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Publications (2)

Proceedings Article | 20 September 2020 Presentation + Paper

Efficient streaming of 3D maps for unmanned navigation: 3D tiling the navigable space and eliminating occluded geometry

Alfred Mayalu, Kevin Kochersberger, Barry Jenkins, Francois Malassenet

Proceedings Volume 11535, 115350B (2020) https://doi.org/10.1117/12.2572182

KEYWORDS: Navigation systems, LIDAR, Sensors, Unmanned systems, Environmental sensing, Visibility, Situational awareness sensors, Satellite navigation systems, Data storage, Non-line-of-sight propagation

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Ground operations in urban environments present a considerable risk to dismounted and mobile tactical teams. Special operations, counterterrorism, and other missions require forces to operate in complex, densely occluded urban environments. In these situations, an adversary can often exploit in-depth local knowledge of the 3D structure of the environment to establish optimal cover, maintain concealed ring positions, and channelize the attacking force into exposed ingress routes. Small Unmanned Aerial Systems (UAS) operating at low altitude and indoors could potentially provide real-time situational awareness in these conditions but are constrained by the limited availability of GPS in these environments and by a lack of detailed 3D map data required for precision sensor-based navigation in complex environments. This paper, based on our prior work in¹ introduces first responder and militaristic applications for an open- source framework for the rapid processing, exploitation, and dissemination of 3D mapping and navigational data at the tactical edge of the network. The proposed protocol is called 3D Tiles Nav which includes certain navigation cell and visibility metadata in order to improve navigational performance and autonomy in complex environments. The navigation cell data structures organizes the low airspace (below 400ft) in which 3D Tiles NAV data is embedded. The resulting data organization supports efficient data delivery, especially in densely occluded (e.g. low altitude and ground level) use cases. In addition, the resulting metadata fuses information about the visible and navigable structure of the operating environment to provide a unique informational framework for planning and conducting missions in densely occluded, high-threat environments. We demonstrate through experiments the extraction of planar features from point cloud data and illustrate through simulations the reduction network, compute, storage, and sensor requirements made possible using our approach.

Proceedings Article | 3 May 2018 Presentation + Paper

Unattended sensor using deep machine learning techniques for rapid response applications

Alfred Mayalu, Kevin Kochersberger

Proceedings Volume 10643, 106430B (2018) https://doi.org/10.1117/12.2304993

KEYWORDS: Machine learning, Neurons, Sensors, Acoustics, Feature extraction, Unattended sensors, Prototyping, Intelligent sensors, Fourier transforms, Data storage

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