基于单个飞秒激光器的电学异步采样实现精密快速 LiDAR
Precise and Fast LIDAR via Electrical Asynchronous Sampling Based on a Single Femtosecond Laser
AI总结：
提出基于单台飞秒光学频率梳的异步电脉冲采样 LiDAR，克服采样混叠与低数据利用率限制，在米级非合作目标上实现微米级精度与兆赫兹更新率，并演示高速计量、百万像素每秒三维成像和非接触生命体征检测。
摘要：
LiDAR 利用基于激光的测距方法进行精密环境三维传感，在科学和工业中具有众多应用。然而，传统 LiDAR 面临难以同时实现高测距精度和快速测量速率的挑战，这限制了其在航空航天、智能医疗等更精密领域的适用性。通过在具有稳定重复频率和飞秒脉冲宽度的单个光学频率梳上采用异步电脉冲采样策略，我们利用了光学频率梳测距方法的优势，并克服了传统方法固有的采样混叠和数据利用率低的限制。这使 LiDAR 的性能显著提升，能够在米级距离内对非合作目标实现微米级精度和兆赫兹量级更新率。具体而言，基于一台 56.091 MHz 飞秒激光器，我们在 1 MHz 更新率下实现了 38.8 μm 的 Allan 偏差，并在 2 ms 时间平均后实现了 8.06 μm 的 Allan 偏差。这种增强支持多种先进测量应用，包括对高速物体的计量监测、1 百万像素/秒的精密三维扫描成像，以及首次使用飞行时间 LiDAR 进行非接触式生命体征检测。该 LiDAR 为多个领域的精密、快速实时测量开启了新的可能性。
英文摘要：
Using a laser-based ranging method for precise environmental 3D sensing, LiDAR has numerous applications in science and industry. However, conventional LiDAR face challenges in simultaneously achieving high ranging precision and fast measurement rates, which limits their applicability in more precise fields, such as aerospace, smart healthcare and beyond. By employing an asynchronous electrical pulse sampling strategy on a single optical frequency comb with a stable repetition rate and femtosecond-pulse width, we exploit the advantages of optical-frequency-comb ranging method and overcome the limitations of sampling aliasing and low data-utilization inherent in traditional approaches. This enables a significant improvement of LiDAR's performance to achieve micrometer-level precision and megahertz-regimes update rates over meter-range on non-cooperative targets. Specifically, we achieve 38.8-$μ$m Allan deviation at 1-MHz update rate and 8.06-$μ$m Allan deviation after 2-ms time-averaging based on a 56.091-MHz femtosecond laser. This enhancement enables various advanced measurement applications, including metrology monitoring on high-speed objects, 1-megapixel/s precise 3D scanning imaging and first-ever contactless vital sign detection using time-of-flight LiDAR. This LiDAR unlock new possibilities for precise and fast real-time measurements in diverse fields.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
