Frequency Modulated Continuous Wave Compressive Depth Mapping

Authors

Daniel J. Lum, University of Rochester
Samuel H. Knarr, University of Rochester
John C. Howell, Chapman UniversityFollow

Document Type

Conference Proceeding

Publication Date

6-4-2018

Abstract

We present an inexpensive architecture for converting a frequency-modulated continuous-wave LiDAR system into a compressive-sensing based depth-mapping camera. Instead of raster scanning to obtain depth-maps, compressive sensing is used to significantly reduce the number of measurements. Ideally, our approach requires two difference detectors. Due to the large flux entering the detectors, the signal amplification from heterodyne detection, and the effects of background subtraction from compressive sensing, the system can obtain higher signal-to-noise ratios over detector-array based schemes while scanning a scene faster than is possible through raster-scanning. Moreover, by efficiently storing only 2m data points from m < n measurements of an n pixel scene, we can easily extract depths by solving only two linear equations with efficient convex-optimization methods.

Comments

This article was originally presented at Frontiers in Optics in 2018. https://doi.org/10.1364/OE.26.015420

Recommended Citation

Frequency Modulated Continuous Wave Compressive Depth Mapping, Daniel J. Lum and J. C. Howell, Frontiers in Optics, FM3C. 5.

Peer Reviewed

1

Copyright

Optica