基于自编码器的SPAD直接ToF激光雷达鲁棒压缩直方图

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Lichen Feng;Yimeng Liu;Dong Li;Yaoqi Bao;Rui Ma;Zhangming Zhu
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引用次数: 0

摘要

由于使用时间相关单光子计数(TCSPC),用于光探测和测距(LiDAR)的高分辨率单光子雪崩二极管(SPAD)传感器阵列芯片在处理高数据速率方面面临挑战。片上部分直方图方法存在压缩比低、延时高等缺点。无计数/直方图方法具有较高的噪声敏感性。直接压缩接收到的光子已经实现了高cr,但依赖于手工制作的分析码本(CBs),并且缺乏鲁棒性。本文提出了一种基于自编码器的鲁棒数据驱动压缩直方图方法,该方法比以前的压缩方法具有更高的精度,能够覆盖具有挑战性的成像场景。此外,提出并在FPGA上实现了每个照明至少可处理64个时间戳的紧凑4位量化压缩引擎设计。将引擎与我们先前的SPAD阵列芯片连接后,构建了线扫描LiDAR系统。与基于tcspc的全直方图电路相比,在保持相似深度精度的同时,存储器大小减少了3.76倍。与基于扫描的部分直方图电路相比,所提出的设计实现了CR的4倍提高,均方根深度误差(RMSE)降低了65%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Compressive Histogramming Based on Autoencoder for SPAD Direct ToF LiDAR Covering Challenging Scenarios
The high-resolution single-photon avalanche diode (SPAD) sensor array chip for light detection and ranging (LiDAR) faces a challenge in handling high data rates, due to the use of time-correlated single-photon counting (TCSPC). On-chip partial histogramming methods suffer from low compression ratio (CR) and high latency. Count/histogram-less methods are with high noise-sensitivity. Direct compression on received photons has achieved high CRs but relies on handcrafted analytical codebooks (CBs) and lacks robustness. This article proposes a robust and data-driven compressive histogramming method based on the autoencoder, which shows superior accuracy improvement over previous compression methods, capable of covering challenging imaging scenarios. Furthermore, the compact 4-bit quantized compression engine design that can process at least 64 timestamps per illumination is proposed and implemented in FPGA. A line scanning LiDAR system is constructed after connecting the engine with our previous SPAD array chip. Compared to the TCSPC-based full histogram circuit, a memory size reduction of $3.76\times $ is achieved while maintaining similar depth accuracy. Compared to the sweep-based partial histogramming circuit, the proposed design achieves a $4\times $ improvement in CR and a 65% reduction in root-mean-square depth error (RMSE).
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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