Surface material recognition through machine learning using time of flight LiDAR

Abstract

We explore an active illumination approach for remote and obscured material recognition, based on quantum parametric mode sorting and single-photon detection. By raster scanning a segment of material, we capture the relationships between each mirror position’s peak count and location. These features allow for a robust measurement of a material’s relative reflectance and surface texture. Through inputting these identifiers into machine learning algorithms, a high accuracy of 99% material recognition can be achieved, even maintaining up to 89.17% accuracy when materials are occluded by a lossy and multi-scattering obscurant of up to 15.2 round-trip optical depth.