{"title":"基于自编码器的SPAD直接ToF激光雷达鲁棒压缩直方图","authors":"Lichen Feng;Yimeng Liu;Dong Li;Yaoqi Bao;Rui Ma;Zhangming Zhu","doi":"10.1109/JSEN.2025.3575784","DOIUrl":null,"url":null,"abstract":"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 <inline-formula> <tex-math>$3.76\\times $ </tex-math></inline-formula> is achieved while maintaining similar depth accuracy. Compared to the sweep-based partial histogramming circuit, the proposed design achieves a <inline-formula> <tex-math>$4\\times $ </tex-math></inline-formula> improvement in CR and a 65% reduction in root-mean-square depth error (RMSE).","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"27701-27711"},"PeriodicalIF":4.3000,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust Compressive Histogramming Based on Autoencoder for SPAD Direct ToF LiDAR Covering Challenging Scenarios\",\"authors\":\"Lichen Feng;Yimeng Liu;Dong Li;Yaoqi Bao;Rui Ma;Zhangming Zhu\",\"doi\":\"10.1109/JSEN.2025.3575784\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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 <inline-formula> <tex-math>$3.76\\\\times $ </tex-math></inline-formula> is achieved while maintaining similar depth accuracy. Compared to the sweep-based partial histogramming circuit, the proposed design achieves a <inline-formula> <tex-math>$4\\\\times $ </tex-math></inline-formula> improvement in CR and a 65% reduction in root-mean-square depth error (RMSE).\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"25 14\",\"pages\":\"27701-27711\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11028946/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/11028946/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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).
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:
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