SPAD-Based Flash Lidar with High Background Light Suppression

2017 ◽  
pp. 37-43
Author(s):  
Olaf M. Schrey ◽  
Maik Beer ◽  
Werner Brockherde ◽  
Bedrich J. Hosticka
Keyword(s):  
High Background ◽  
Background Light ◽  
Flash Lidar

A Three-Phase, One-Tap High Background Light Subtraction Time-of-Flight Camera

2019 ◽  
Vol 66 (6) ◽  
pp. 2219-2229 ◽  
Author(s):  
Chandani Anand ◽  
Kapil Jainwal ◽  
Mukul Sarkar
Keyword(s):  
Time Of Flight ◽  
High Background ◽  
Background Light ◽  
Three Phase

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

2022 ◽  
Author(s):  
Gongbo Chen ◽  
Felix Landmeyer ◽  
Christian Wiede ◽  
Rainer Kokozinski
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Single Photon ◽  
Photon Counting ◽  
Recovery Process ◽  
Digital Processing ◽  
Target Distance ◽  
Learning Ability ◽  
High Background ◽  
Background Light

Abstract 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.

Optical Wireless APD Receiver With High Background-Light Immunity for Increased Communication Distances

2016 ◽  
Vol 51 (7) ◽  
pp. 1663-1673 ◽  
Author(s):  
Paul Brandl ◽  
Tomislav Jukic ◽  
Reinhard Enne ◽  
Kerstin Schneider-Hornstein ◽  
Horst Zimmermann
Keyword(s):  
Optical Wireless ◽  
High Background ◽  
Background Light

scholarly journals Improvement of Accuracy and Precision of the LiDAR System Working in High Background Light Conditions

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 45
Author(s):  
Thanh-Tuan Nguyen ◽  
Ching-Hwa Cheng ◽  
Don-Gey Liu ◽  
Minh-Hai Le
Keyword(s):  
Success Rate ◽  
Cross Correlation ◽  
Estimation Methods ◽  
Correlation Technique ◽  
Lidar System ◽  
High Background ◽  
Single Beam ◽  
Background Light ◽  
Accuracy And Precision ◽  
Receiver System

Background light noise is one of the major challenges in the design of Light Detection and Ranging (LiDAR) systems. In this paper, we build a single-beam LiDAR module to investigate the effect of light intensity on the accuracy/precision and success rate of measurements in environments with strong background noises. The proposed LiDAR system includes the laser signal emitter and receiver system, the signal processing embedded platform, and the computer for remote control. In this study, two well-known time-of-flight (ToF) estimation methods, which are peak detection and cross-correlation (CC), were applied and compared. In the meanwhile, we exploited the cross-correlation technique combined with the reduced parabolic interpolation (CCP) algorithm to improve the accuracy and precision of the LiDAR system, with the analog-to-digital converter (ADC) having a limited resolution of 125 mega samples per second (Msps). The results show that the CC and CCP methods achieved a higher success rate than the peak method, which is 12.3% in the case of applying emitted pulses 10 µs/frame and 8.6% with 20 µs/frame. In addition, the CCP method has the highest accuracy/precision in the three methods reaching 7.4 cm/10 cm and has a significant improvement over the ADC’s resolution of 1.2 m. This work shows our contribution in building a LiDAR system with low cost and high performance, accuracy, and precision.

scholarly journals Background Light Rejection in SPAD-Based LiDAR Sensors by Adaptive Photon Coincidence Detection

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4338 ◽  
Author(s):  
Maik Beer ◽  
Jan Haase ◽  
Jennifer Ruskowski ◽  
Rainer Kokozinski
Keyword(s):  
Dynamic Range ◽  
Single Photon ◽  
Event Rate ◽  
Low Cost ◽  
Autonomous Driving ◽  
Cmos Technology ◽  
Coincidence Detection ◽  
Driver Assistance Systems ◽  
High Background ◽  
Background Light

Light detection and ranging (LiDAR) systems based on silicon single-photon avalanche diodes (SPAD) offer several advantages, like the fabrication of system-on-chips with a co-integrated detector and dedicated electronics, as well as low cost and high durability due to well-established CMOS technology. On the other hand, silicon-based detectors suffer from high background light in outdoor applications, like advanced driver assistance systems or autonomous driving, due to the limited wavelength range in the infrared spectrum. In this paper we present a novel method based on the adaptive adjustment of photon coincidence detection to suppress the background light and simultaneously improve the dynamic range. A major disadvantage of fixed parameter coincidence detection is the increased dynamic range of the resulting event rate, allowing good measurement performance only at a specific target reflectance. To overcome this limitation we have implemented adaptive photon coincidence detection. In this technique the parameters of the photon coincidence detection are adjusted to the actual measured background light intensity, giving a reduction of the event rate dynamic range and allowing the perception of high dynamic scenes. We present a 192 × 2 pixel CMOS SPAD-based LiDAR sensor utilizing this technique and accompanying outdoor measurements showing the capability of it. In this sensor adaptive photon coincidence detection improves the dynamic range of the measureable target reflectance by over 40 dB.

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