Lum Daniel J, Knarr Samuel H, Howell John C
Opt Express. 2018 Jun 11;26(12):15420-15435. doi: 10.1364/OE.26.015420.
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.
我们提出了一种低成本架构，用于将调频连续波激光雷达系统转换为基于压缩感知的深度映射相机。该架构并非采用光栅扫描来获取深度图，而是利用压缩感知显著减少测量次数。理想情况下，我们的方法需要两个差分探测器。由于进入探测器的光通量较大、外差检测带来的信号放大以及压缩感知中背景减除的影响，该系统在扫描场景时能够比基于探测器阵列的方案更快，同时获得更高的信噪比。此外，通过仅高效存储来自n像素场景的m < n次测量中的2m个数据点，我们可以通过高效的凸优化方法求解仅两个线性方程，轻松提取深度信息。
