Year: 2024
Type: article
Abstract: Light detection and ranging (LIDAR) sensors using a polarization-diverse receiver are able to capture polarimetric information about the target under measurement. We demonstrate this capability using a silicon photonic receiver architecture that enables this on a shot-by-shot basis, enabling polarization analysis nearly instantaneously in the point cloud, and then use this data to train a material classification neural network. Using this classifier, we show an accuracy of 85.4% for classifying plastic, wood, concrete, and coated aluminum.
Source: Sensors
DOI: 10.3390/s24175761
Authors: Cibby Pulikkaseril, Duncan Ross, Alexander Tofini, Yannick K. Lize, Federico Collarte
Language: English
Subfield: Instrumentation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
Funders: Johns Hopkins University, Strong
OpenAlex ID: W4402282900