嵌入式直接飞行时间传感中一位频率探测的深度动态。

IF 18.6
Seth Lindgren, Benjamin R Johnson, Lucas J Koerner
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引用次数: 0

摘要

带有单光子雪崩二极管(spad)的飞行时间(ToF)传感器通过积累光子返回时间的直方图来估计深度,这抛弃了测量深度动态(如振动或瞬态运动)所需的时间信息。我们介绍了一种将直接ToF传感器转换为深度频率分析仪的方法,该分析仪能够仅使用轻量级的传感器上计算来测量高频运动和瞬态事件。通过将传统的离散傅立叶变换(dft)替换为通过过采样σ - δ调制生成的1位探测正弦波,我们无需乘法器或浮点运算即可实现像素内频率分析。我们将深度动态的轻量级分析扩展到Haar小波,用于时间局部检测短暂的,非重复的深度变化。我们通过仿真和硬件实验验证了我们的方法,表明它实现了接近全分辨率dft的噪声性能,检测了6 kHz以上的亚毫米运动,并定位了毫秒级瞬态。使用实验室ToF设置,我们演示了在振荡运动分析和深度边缘检测中的应用。这项工作有可能使新型紧凑型运动感知ToF传感器成为可能,用于工业预测性维护、结构健康监测、机器人感知和动态场景理解等领域的嵌入式部署。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Depth Dynamics via One-Bit Frequency Probing in Embedded Direct Time-of-Flight Sensing.

Time-of-flight (ToF) sensors with single-photon avalanche diodes (SPADs) estimate depth by accumulating a histogram of photon return times, which discards the timing information required to measure depth dynamics, such as vibrations or transient motions. We introduce a method that transforms a direct ToF sensor into a depth frequency analyzer capable of measuring high-frequency motion and transient events using only lightweight, on-sensor computations. By replacing conventional discrete Fourier transforms (DFTs) with one-bit probing sinusoids generated via oversampled sigma-delta modulation, we enable in-pixel frequency analysis without multipliers or floating-point operations. We extend the lightweight analysis of depth dynamics to Haar wavelets for time-localized detection of brief, non-repetitive depth changes. We validate our approach through simulation and hardware experiments, showing that it achieves noise performance approaching that of full-resolution DFTs, detects sub-millimeter motions above 6 kHz, and localizes millisecond-scale transients. Using a laboratory ToF setup, we demonstrate applications in oscillatory motion analysis and depth edge detection. This work has the potential to enable a new class of compact, motion-aware ToF sensors for embedded deployment in industrial predictive maintenance, structural health monitoring, robotic perception, and dynamic scene understanding.

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