Non-line-of-sight snapshots and background mapping with an active corner camera

Sheila Seidel, Hoover Rueda-Chacón, Iris Cusini, Federica Villa, Franco Zappa, Christopher Yu and Vivek K Goyal ()
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Sheila Seidel: Boston University
Hoover Rueda-Chacón: Boston University
Iris Cusini: Dip. Elettronica, Informazione e Bioingegneria, Politecnico di Milano
Federica Villa: Dip. Elettronica, Informazione e Bioingegneria, Politecnico di Milano
Franco Zappa: Dip. Elettronica, Informazione e Bioingegneria, Politecnico di Milano
Christopher Yu: Charles Stark Draper Laboratory
Vivek K Goyal: Boston University

Nature Communications, 2023, vol. 14, issue 1, 1-7

Abstract: Abstract The ability to form reconstructions beyond line-of-sight view could be transformative in a variety of fields, including search and rescue, autonomous vehicle navigation, and reconnaissance. Most existing active non-line-of-sight (NLOS) imaging methods use data collection steps in which a pulsed laser is directed at several points on a relay surface, one at a time. The prevailing approaches include raster scanning of a rectangular grid on a vertical wall opposite the volume of interest to generate a collection of confocal measurements. These and a recent method that uses a horizontal relay surface are inherently limited by the need for laser scanning. Methods that avoid laser scanning to operate in a snapshot mode are limited to treating the hidden scene of interest as one or two point targets. In this work, based on more complete optical response modeling yet still without multiple illumination positions, we demonstrate accurate reconstructions of foreground objects while also introducing the capability of mapping the stationary scenery behind moving objects. The ability to count, localize, and characterize the sizes of hidden objects, combined with mapping of the stationary hidden scene, could greatly improve indoor situational awareness in a variety of applications.

Date: 2023
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DOI: 10.1038/s41467-023-39327-2

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