An Efficient Adaptive Noise Removal Filter on Range Images for LiDAR Point Clouds

Light Detection and Ranging (LiDAR) is a critical sensor for autonomous vehicle systems, providing high-resolution distance measurements in real-time. However, adverse weather conditions such as snow, rain, fog, and sun glare can affect LiDAR performance, requiring data preprocessing. This paper proposes a novel approach, the Adaptive Outlier Removal filter on range Image (AORI), which combines a projection image from LiDAR point clouds with an adaptive outlier removal filter to remove snow particles. Our research aims to analyze the characteristics of LiDAR and propose an image-based approach derived from LiDAR data that addresses the limitations of previous studies, particularly in improving the efficiency of nearest neighbor point search. Our proposed method achieves outstanding performance in both accuracy (>96%) and processing speed (0.26 s per frame) for autonomous driving systems under harsh weather from raw LiDAR point clouds in the Winter Adverse Driving dataset (WADS). Notably, AORI outperforms state-of-the-art filters by achieving a 6.6% higher F1 score and 0.7% higher accuracy. Although our method has a lower recall than state-of-the-art methods, it achieves a good balance between retaining object points and filter noise points from LiDAR, indicating its promise for snow removal in adverse weather conditions

Optimized Pmos-Triggered Bidirectional Scr For Low-Voltage Esd Protection Applications

In this paper, an optimized pMOS-triggered bidirectional silicon-controlled rectifier (PTBSCR) fabricated in a 0.18-μm CMOS technology is proposed as a viable electrostatic discharge (ESD) protection solution. Capable of working under both the power-ON and power-OFF conditions, this structure is verified to provide bidirectional ESD protection performance superior to those reported in the literatures. Critical ESD parameters, such as the trigger voltage, holding voltage, and leakage current, can be flexibly adjusted via layout changes. With a low trigger voltage, a small ESD design window, a high robustness, and a small silicon area consumption, the PTBSCR is very suitable for low-voltage and low-power ESD protection applications. © 2014 IEEE

An Adaptive Group of Density Outlier Removal Filter: Snow Particle Removal from LiDAR Data

Light Detection And Ranging (LiDAR) is an important technology integrated into self-driving cars to enhance the reliability of these systems. Even with some advantages over cameras, it is still limited under extreme weather conditions such as heavy rain, fog, or snow. Traditional methods such as Radius Outlier Removal (ROR) and Statistical Outlier Removal (SOR) are limited in their ability to detect snow points in LiDAR point clouds. This paper proposes an Adaptive Group of Density Outlier Removal (AGDOR) filter that can remove snow particles more effectively in raw LiDAR point clouds, with verification on the Winter Adverse Driving Dataset (WADS). In our proposed method, an intensity threshold combined with a proposed outlier removal filter was employed. Outstanding performance was obtained, with higher accuracy up to 96% and processing speed of 0.51 s per frame in our result. In particular, our filter outperforms the state-of-the-art filter by achieving a 16.32% higher Precision at the same accuracy. However, our method archive is lower in recall than the state-of-the-art method. This clearly indicates that AGDOR retains a significant amount of object points from LiDAR. The results suggest that our filter would be useful for snow removal under harsh weathers for autonomous driving systems

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

Processing speed and accuracy of measurements are important factors reflecting the performance quality of light detection and ranging (LiDAR) systems. This study proposed a fast cross-correlation (fCC) algorithm to improve the computation loading in the LiDAR system operating in high background noise environments. To reduce the calculation time, we accumulated cycles of the receiver waveform to increase the signal-to-noise ratio. In this way, the stop pulse can be easily distinguished from the background noise by applying the cross-correlation (CC) on the accumulated receiver waveform with the first start pulse. In addition, the proposed fCC combined with variant interpolation techniques: the parabolic (fCCP), gaussian (fCCG), cosine (fCCC), and cubic spline (fCCS) to increase the measurement accuracy were also investigated and compared. The experiments were performed on the real-time LiDAR system under high background light intensity. The tested results showed that the proposed method fCCP achieved 879 ns per measurement, 38 times faster than the original CC method combined with the same parabolic interpolation algorithm (CCP) 33.5 μs. Meanwhile, the fCCS method resulted in the highest accuracy/precision, reaching 5.193 cm/8.588 cm, respectively. These results demonstrated that our proposed method significantly improves the measurements speed in the LiDAR system operating under strong background light

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

Processing speed and accuracy of measurements are important factors reflecting the performance quality of light detection and ranging (LiDAR) systems. This study proposed a fast cross-correlation (fCC) algorithm to improve the computation loading in the LiDAR system operating in high background noise environments. To reduce the calculation time, we accumulated cycles of the receiver waveform to increase the signal-to-noise ratio. In this way, the stop pulse can be easily distinguished from the background noise by applying the cross-correlation (CC) on the accumulated receiver waveform with the first start pulse. In addition, the proposed fCC combined with variant interpolation techniques: the parabolic (fCCP), gaussian (fCCG), cosine (fCCC), and cubic spline (fCCS) to increase the measurement accuracy were also investigated and compared. The experiments were performed on the real-time LiDAR system under high background light intensity. The tested results showed that the proposed method fCCP achieved 879 ns per measurement, 38 times faster than the original CC method combined with the same parabolic interpolation algorithm (CCP) 33.5 μs. Meanwhile, the fCCS method resulted in the highest accuracy/precision, reaching 5.193 cm/8.588 cm, respectively. These results demonstrated that our proposed method significantly improves the measurements speed in the LiDAR system operating under strong background light

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

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

Speed Improvement in Image Stitching for Panoramic Dynamic Images during Minimally Invasive Surgery

Minimally invasive surgery (MIS) minimizes the surgical incisions that need to be made and hence reduces the physical trauma involved during the surgical process. The ultimate goal is to reduce postoperative pain and blood loss as well as to limit the scarring area and hence accelerate recovery. It is therefore of great interest to both the surgeon and the patient. However, a major problem with MIS is that the field of vision of the surgeon is very narrow. We had previously developed and tested an MIS panoramic endoscope (MISPE) that provides the surgeon with a broader field of view. However, one issue with the MISPE was its low rate of video stitching. Therefore, in this paper, we propose using the region of interest in combination with the downsizing technique to improve the image-stitching performance of the MISPE. Experimental results confirm that, by using the proposed method, the image size can be increased by more than 160%, with the image resolution also improving. For instance, we could achieve performance improvements of 10× (CPU) and 23× (GPU) as compared to that of the original method

Effects of Nanoscaled Tin-Doped Indium Oxide on Liquid Crystals against Electrostatic Discharge

In our studies, it was confirmed that the cause of image sticking on liquid crystal (LC) cells is based on attacks of electrostatic discharge (ESD), which can be greatly relieved by doping with a small amount of tin-doped indium oxide (ITO) nanoparticles. Our proposed remedy allows the residual time of image sticking to be significantly reduced by more than an order and may protect the LC displays against any adverse ESD conditions, thus enhancing the overall display quality and reliability. In this study, conventional voltage-transmittance (V-T) characterization, voltage holding ratio (VHR) measurement, and ESD testing were employed to investigate the properties of the ITO-doped LCs. Based on our low voltage measurement results, it is interesting to find that ITO nanoparticles do not evidently alter the intrinsic properties of the LC. Namely, ITO additive initiates an early breakdown of the doped LC samples exposed to high electric fields. A model is proposed in this paper to depict the possible role of ITO particles applied in LCs