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

Abstract

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.

Performance analysis of the coherent FMCW photonic radar system under the influence of solar noise

“…In Refs [30,48], many others have proposed the FMCW photonic radar system operating at W-band (75-110 GHz), which is widely used for automotive vehicle radar systems and is investigated under adverse weather conditions such as haze, fog, and rain that limit the detection range performance and precision of the system. In addition, various atmospheric attenuations and effects of solar background noise on the photonic radar system have also been reported by some authors in [37,49]. Along with these published literature works, some commercially available lidar systems are available in the market, which provide single-chip lidar for fast connectivity and improved safety in autonomous mobility as reported in Ref.…”

Metric Reliability Analysis of Autonomous Marine LiDAR Systems under Extreme Wind Loads

“…The model was based on a least squares hexagon fitting approach, which was proven to produce measurement accuracies of 5 cm horizontal and 4 cm vertical. Thanh-Tuan Nguyen et al [14] proposed a low-cost, high-performance LiDAR system by combining the cross-correlation technique with reduced parabolic interpolation (CCP) to improve the accuracy and precision to 7.4 cm and 10 cm, respectively, in the case of limited resolution of analog-to-digital converters. In terms of measurement data denoising, Yijian Zhang et al [15] proposed Ensemble Empirical Mode Decomposition (EEMD) based on Wavelet Transform (WT) and the Locally Weighted Scatterplot Smoothing (LOWESS) method for LiDAR signal denoising, which reduced the signal extraction error to 1.1%.…”

A Fast Cross-Correlation Combined with Interpolation Algorithms for the LiDAR Working in the High Background Noise

“…For instance, de-noising the LiDAR signal by the variant mode decomposition combined with a whale optimization algorithm is proposed in [15], noise improvement by three different digital filtering methods the finite response low pass filter, the empirical mode decomposition filter, and Savitzky-Golay is suggested in [16], multi-class support vector machines and artificial neural network methods are compared in [17], and an approach to predict the performance of LiDAR in sunlight using the dimensionless parameter is implemented in [18]. The compound software algorithms also increase the execution time and significantly reduce the resolution of LiDAR, which are challenges for LiDAR system designers [2,10,19,20].…”

“…These pulse detection methods perform quickly and accurately under ideal conditions with low background noise. However, since LiDAR often works in environments with high background noise and bad weather, causing the amplitude and width of the stopping pulse to be distorted and overlapped, the pulse detection methods are greatly affected and ineffective [20,30,33]. IToF algorithm is applied to signals in the full waveform.…”

“…Crosscorrelation (CC) is an IToF method of time delay estimation that relies on the maximum correlation coefficient between the transmitted and received signals. Compared with pulse detection methods, the CC applies more effectively in measurement systems operating in high noise environments [20,[34][35][36][37]. One disadvantage of the CC method is that it is applied to signals in a full waveform, so this algorithm needs to process a large amount of data.…”