Compact Real-Time Inter-Frame Histogram Builder for 15-Bits High-Speed ToF-Imagers Based on Single-Photon Detection

Vornicu, Ion; Darie, Angela; Carmona-Galán, Ricardo; Rodríguez-Vázquez, Á.

doi:10.1109/jsen.2018.2885960

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…In Seo [ 57 ], a PH is obtained through the combination of 5-bit coarse histogram for peak detection and a 6-bit fine histogram for fine resolution. A similar solution, implemented in a field-programmable gate-Array (FPGA), is presented in [ 72 ]. In that case, two separate histograms are computed, dividing the timestamp value in two parts: the coarse histogram (CH) considers only the MSBs of all the timestamps provided by the TDC, and it is used to find the peak of the distribution; whereas the fine histogram (FH) considers just the LSBs of timestamps centered around the peak, and it is used to achieve fine depth resolution.…”

Section: Spad and Sipm Detectors For Pulsed-lidarmentioning

confidence: 99%

SPADs and SiPMs Arrays for Long-Range High-Speed Light Detection and Ranging (LiDAR)

Villa

Severini

Madonini

et al. 2021

Sensors

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Light Detection and Ranging (LiDAR) is a 3D imaging technique, widely used in many applications such as augmented reality, automotive, machine vision, spacecraft navigation and landing. Achieving long-ranges and high-speed, most of all in outdoor applications with strong solar background illumination, are challenging requirements. In the introduction we review different 3D-ranging techniques (stereo-vision, projection with structured light, pulsed-LiDAR, amplitude-modulated continuous-wave LiDAR, frequency-modulated continuous-wave interferometry), illumination schemes (single point and blade scanning, flash-LiDAR) and time-resolved detectors for LiDAR (EM-CCD, I-CCD, APD, SPAD, SiPM). Then, we provide an extensive review of silicon- single photon avalanche diode (SPAD)-based LiDAR detectors (both commercial products and research prototypes) analyzing how each architecture faces the main challenges of LiDAR (i.e., long ranges, centimeter resolution, large field-of-view and high angular resolution, high operation speed, background immunity, eye-safety and multi-camera operation). Recent progresses in 3D stacking technologies provided an important step forward in SPAD array development, allowing to reach smaller pitch, higher pixel count and more complex processing electronics. In the conclusions, we provide some guidelines for the design of next generation SPAD-LiDAR detectors.

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Section: Spad and Sipm Detectors For Pulsed-lidarmentioning

confidence: 99%

SPADs and SiPMs Arrays for Long-Range High-Speed Light Detection and Ranging (LiDAR)

Villa

Severini

Madonini

et al. 2021

Sensors

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“…Considering that one measurement period is extremely short and that the detection array contains at least hundreds of SPADS, a LiDAR system can generate large amounts of data each second. An estimated data amount according to the size of the SPAD array and the sensor resolution is given in [44]. However, the data throughput and the storage capacity are restricted in ADAS.…”

Section: Data Compressionmentioning

confidence: 99%

Data Processing Approaches on SPAD-Based d-TOF LiDAR Systems: A Review

2021

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With the rise of advanced driver assistance systems (ADAS), range sensors and their data processing methods are becoming more and more important. Light detection and ranging (LiDAR) sensors are attracting attention due to their unique advantages in terms of radial distance resolution and detection range. However, the study of LiDAR data processing is usually divorced from the LiDAR sensor measurement process itself. This leads to critical measurement information being overlooked. This paper seeks a breakthrough to improve the performance of singlephoton-avalanche-diode-based direct time-of-flight LiDAR systems by reviewing the data processing stages and corresponding processing approaches for LiDAR measurements, starting from photon incidence and ending with high-level feature recognition. Firstly, we propose a LiDAR system model based on data generation and transfer. The data forms in such a LiDAR system are mainly classified into timestamps, time-correlated histograms, point cloud data, and high-level properties. Subsequently, data processing methods applied to each of these data forms are analyzed. A number of hardware solutions closely related to data transmission and control are also included in the discussion. The principles, limitations, and challenges of these methods are discussed in detail and the criteria for evaluation of time-correlated histograms in ADAS are proposed. Finally, the research gaps in data processing are summarized, and future directions for research development are presented.

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“…The SPAD-based LiDAR system determines the distance by time-of-flight (TOF) measurements. However, except for the desired laser photons, the SPAD can be easily triggered by background photons as well because of its high sensitivity [4]. Therefore, the greatest challenge of a SPAD-based LiDAR system is background light [5].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and neural network-based multi-peak analysis on time-correlated LiDAR histograms

Chen

Landmeyer

Wiede

et al. 2022

J. Opt.

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Time correlated single photon counting (TCSPC) is a statistical method to generate time-correlated histograms (TC-Hists), which are based on the time-of-flight (TOF) information measured by photon detectors such as single-photon avalanche diodes. With restricted measurements per histogram and the presence of high background light, it is challenging to obtain the target distance in a TC-Hist. In order to improve the data processing robustness under these conditions, the concept of machine learning is applied to the TC-Hist. Using the neural network-based multi-peak analysis (NNMPA), introduced by us, including a physics-guided feature extraction, a neural network multi-classifier, and a distance recovery process, the analysis is focused on a small amount of critical features in the TC-Hist. Based on these features, possible target distances with correlated certainty values are inferred. Furthermore, two optimization approaches regarding the learning ability and real-time performance are discussed. In particular, variants of the NNMPA are evaluated on both synthetic and real datasets. The proposed method not only has higher robustness in allocating the coarse position (±5 %) of the target distance in harsh conditions, but also is faster than the classical digital processing with an average-filter. Thus, it can be applied to improve the system robustness, especially in the case of high background light and middle-range detections.

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Compact Real-Time Inter-Frame Histogram Builder for 15-Bits High-Speed ToF-Imagers Based on Single-Photon Detection

Cited by 10 publications

References 12 publications

SPADs and SiPMs Arrays for Long-Range High-Speed Light Detection and Ranging (LiDAR)

SPADs and SiPMs Arrays for Long-Range High-Speed Light Detection and Ranging (LiDAR)

Data Processing Approaches on SPAD-Based d-TOF LiDAR Systems: A Review

Feature extraction and neural network-based multi-peak analysis on time-correlated LiDAR histograms

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