Demonstration on range imaging of 256    ×    256 pixels and 30 frames per second using short wavelength infrared pulsed time-of-flight laser sensor with linear array receiver

Kameyama, Shumpei; Hirai, Akihito; Imaki, Masaharu; Tsuji, Hiroyuki; Nishino, Yuichi; Ito, Yusuke; Takabayashi, M.; Tamagawa, Yasuhisa; Nakaji, M.; Ishimura, E.; Hirano, Y.

doi:10.1117/1.oe.56.3.031214

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The peak detection and sample hold (PDSH) circuit is important in the pulsed time of flight (ToF) light detection and ranging (LiDAR) system [1][2][3][4][5]. The PDSH circuit captures the pulse signal and obtains the accurate amplitude of the signal, which provides precise distance information for the ToF LiDAR system.…”

Section: Introductionmentioning

confidence: 99%

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Modeling, Prediction, and Results Correction of PDSH Circuits for Nanosecond Pulse Peak Detection

Shu,

Du,

Zhao

et al. 2023

Applied Sciences

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The peak detect and sample hold (PDSH) circuit is an important component of the light detection and ranging (LiDAR) system, because the PDSH circuit can correct the walk error of the time discriminator, and distinguish different objects at the same location. However, due to the requirement for high-precision measurement of nanosecond-scale pulse signals, the design of the PDSH circuit presents significant challenges. This paper accomplishes the modeling of the PDSH circuit, including the modeling of the PDSH measurement speed and the voltage step, quantifies various factors that affect the PDSH circuit measurement results, and makes predictions based on the model. Through the derivation and simplification of the rise process in the PDSH circuit, this study establishes a mathematical model to predict the performance of the PDSH circuit. Furthermore, this study also investigates the effect of circuit delay time on the measurement speed of the PDSH circuit. Meanwhile, this study analyzes the voltage step process, and an equivalent circuit model is derived through theoretical analysis. By utilizing a fitting method, the equivalent capacitance value of the diode is obtained. The simulation and experiment results show that the errors between the measurement speed model of the PDSH circuit and the actual circuit are no more than 1.98 ns and 2.03 ns, respectively. Thus, the measurement speed model can accurately predict whether the PDSH circuit meets the requirements. Moreover, the maximum absolute error of the equivalent circuit model is 19.87 mV, which can significantly reduce the error caused by the voltage step.

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Section: Introductionmentioning

confidence: 99%

“…The actual OTA devices exhibit a linear offset in the output current in the saturation region, denoted by k. Therefore, the output current Io in the saturation region can be described (3) and (4). ( )…”

mentioning

confidence: 99%

Modeling, Prediction, and Results Correction of PDSH Circuits for Nanosecond Pulse Peak Detection

Shu,

Du,

Zhao

et al. 2023

Applied Sciences

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“…Some of the popular sensors are Dynamic Vision Sensor (DVS) [2], Asynchronous Timebased Image Sensor (ATIS) [3], the sensitive DVS [4], the Dynamic and Active pixel Vision Sensor (DAVIS) [5] and the Celex-IV [6]. Different from conventional frame-based imaging sensors that work by sampling the scene at a fixed temporal rate (typically 30 frames per second [7]), these neuromorphic sensors detect dynamic changes in illumination. This results in a higher dynamic range, higher sampling rate, and lower power consumption.…”

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confidence: 99%

SeqXFilter: A Memory-efficient Denoising Filter for Dynamic Vision Sensors

Guo,

Wang,

Chen

et al. 2020

Preprint

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Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imaging sensors. However, they are sensitive to background activity (BA) events that are unwanted. There are some filters for tackling this problem based on spatio-temporal correlation. However, they are either memory-intensive or computing-intensive. We propose SeqXFilter, a spatio-temporal correlation filter with only a past event window that has an O(1) space complexity and has simple computations. We explore the spatial correlation of an event with its past few events by analyzing the distribution of the events when applying different functions on the spatial distances. We find the best function to check the spatio-temporal correlation for an event for SeqXFilter, best separating real events and noise events. We not only give the visual denoising effect of the filter but also use two metrics for quantitatively analyzing the filter's performance. Four neuromorphic event-based datasets, recorded from four DVS with different output sizes, are used for validation of our method. The experimental results show that SeqXFilter achieves similar performance as baseline NNb filters, but with extremely small memory cost and simple computation logic. IntroductionResearch on neuromorphic event-based sensors ("silicon retinae") started a few decades back [1]. Recently, the technology has matured to a stage where there have been some commercially available sensors. Some of the popular sensors are Dynamic Vision Sensor (DVS) [2], Asynchronous Timebased Image Sensor (ATIS) [3], the sensitive DVS [4], the Dynamic and Active pixel Vision Sensor (DAVIS) [5] and the Celex-IV [6]. Different from conventional frame-based imaging sensors that work by sampling the scene at a fixed temporal rate (typically 30 frames per second [7]), these neuromorphic sensors detect dynamic changes in illumination. This results in a higher dynamic range, higher sampling rate, and lower power consumption. They have been utilized in applications such as pose estimation [8], gesture-based remote control [9], corner detection [10], and drones [11].However, these sensors will produce background activity (BA) events under constant illumination, which are caused by temporal noise and junction leakage currents [2,12,13]. There are already multiple noise filtering methods for event-based data available.The most commonly employed method is the Nearest Neighbor (NNb) filter based on spatio-temporal correlation [12,14,15]. However, these spatio-temporal NNb filters are memory intensive. For a DVS with N×N pixels, the minimum space complexity is at O(N) level, which puts a burden on the limited programmable logic (PL) embedded in the sensor head. There are two main reasons for the high space complexity of the current spatio-temporal filters. First, the memory size of these filters are dependent on the DVS output size N for the convenience of detecting spatio-temporal Preprint. Under review.

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A Linear-Array Receiver Analog Front-End Circuit for Rotating Scanner LiDAR Application

Zheng

Liu

et al. 2019

IEEE Sensors J.

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Demonstration on range imaging of 256 × 256 pixels and 30 frames per second using short wavelength infrared pulsed time-of-flight laser sensor with linear array receiver

Cited by 4 publications

References 12 publications

Modeling, Prediction, and Results Correction of PDSH Circuits for Nanosecond Pulse Peak Detection

Modeling, Prediction, and Results Correction of PDSH Circuits for Nanosecond Pulse Peak Detection

SeqXFilter: A Memory-efficient Denoising Filter for Dynamic Vision Sensors

A Linear-Array Receiver Analog Front-End Circuit for Rotating Scanner LiDAR Application

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