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Optimized Low Size, Weight, Power and Cost (SWaP-C) Payload for Mapping Interiors and Subterranean on an Unmanned Ground Vehicle

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Technical Report

Corporate Author:

ERDC Geospatial Research Laboratory Alexandria United States

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Section 3 of the FY15 Force 2025 Maneuvers Annual Report indicates that in Dense Urban Areas DUA, specifically in a subsurface, surface, or super-surface structure, the ability to identify threats will be diminished. Most commercially available LIght Detection And Ranging LIDAR systems are specifically designed for high-resolution aerial imaging and mapping applications. As a result, they tend to be large, heavy, power-hungry, data bandwidth intensive, and expensive. They also employ lasers that are not typically eye-safe, which limits their overall effectiveness in subterranean and the interiors of subsurface or super-surface structures. However, due to recent advances in the automotive industry, there are new generations of Size, Weight, Power, and Cost SWaP-C sensors that are eye-safe, making them suitable for use indoors and in subterranean environments. While these tradeoffs limit their effective use to hundreds of meters compared to kilometers for their more expensive counterparts, they are ideal candidates for use in subterranean and building interiors. While cameras fill this niche to some extent, the volumetric calculations provided by these sensors provide additional intelligence to shape the security of the environment and offer more precision when maneuvering troops. These sensors would provide the warfighter with situational understanding in previously inaccessible locations. Therefore, to aid in the Armys need to obtain and maintain situational understanding in DUAs, the authors propose utilizing low size, weight, power, and cost SWaP-C sensors, on a robot platform, for surveying and mapping underground structures and building interiors. Rapidnear real-time data processing is possible by utilizing open-source software and commercial off the shelf COTS components. Using the preferred sensor payload autonomously was also explored.

Subject Categories:

  • Optical Detection and Detectors
  • Land and Riverine Navigation and Guidance

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