Frequency-modulated continuous-wave LiDAR compressive depth-mapping

TL;DR

This paper introduces a cost-effective compressive sensing approach for frequency-modulated continuous-wave LiDAR that significantly reduces measurements and computational complexity while achieving high-resolution depth-maps faster than traditional raster-scanning methods.

Contribution

The authors develop a novel LiDAR depth-mapping system using compressive sensing, enabling faster scene scanning with fewer measurements and simplified depth recovery algorithms.

Findings

Higher signal-to-noise ratios compared to detector-array schemes

Faster scene scanning than raster-scanning methods

Efficient depth recovery with minimal computational overhead

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. % but can operate with only one at the cost of doubling the number of measurments. 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, we show how a single total-variation minimization and two fast least-squares minimizations, instead of a single complex nonlinear minimization, can efficiently recover high-resolution depth-maps with minimal computational overhead. 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.