Solid-state Lidar Research Articles

Design of Nanosecond Pulse Laser Diode Array Driver Circuit for LiDAR

The pulse laser emission circuit plays a crucial role as the emission unit of time-of-flight (TOF) LiDAR. This paper proposes a nanosecond-level pulse laser diode array drive circuit for LiDAR, primarily aimed at addressing the issue of high-speed scanning drive for the laser diode array at the emission end of solid-state LiDAR. Based on the single pulse laser diode drive circuit, this paper innovatively designs a circuit that includes modules such as a boost circuit, linear power supply, high-speed gate driver, GaN field-effect transistor, and pulse narrowing circuit, realizing an 8-channel laser diode array drive circuit. This circuit can achieve a pulse laser array drive with a single channel operating frequency of greater than 100 kHz, an output pulse width of less than 5 ns, a peak power greater than 75 W, and a channel switching time that does not exceed 1 μs. A field programmable gate array (FPGA) is used to control the operation of this circuit and perform a series of performance tests. Experimental results show that this circuit has a high repetition rate, large output power, a narrow pulse width, and fast switching speeds, making it highly suitable for use in the optical emission module of solid-state LiDAR.

A NOVEL HYBRID DEEP LEARNING APPROACH FOR 3D OBJECT DETECTION AND TRACKING IN AUTONOMOUS DRIVING

Recently Object detection and tracking using fusion of LiDAR and RGB camera for the autonomous vehicle environment is a challenging task. The existingworks initiates several object detection and tracking frameworks using ArtificialIntelligence (AI) algorithms. However, they were limited with high false positives and computation time issues thus lacking the performance of autonomousdriving environment. The existing issues are resolved by proposing HybridDeep Learning based Multi Object Detection and Tracking (HDL-MODT) using sensor fusion methods. The proposed work performs fusion of solid stateLiDAR, Pseudo LiDAR, and RGB camera for improving detection and trackingquality. At first, the multi-stage preprocessing is done in which noise removal isperformed using Adaptive Fuzzy Filter (A-Fuzzy). The pre-processed fused image is then provided for instance segmentation to reduce the classification andtracking complexity. For that, the proposed work adopts Lightweight GeneralAdversarial Networks (LGAN). The segmented image is provided for objectdetection and tracking using HDL. For reducing the complexity, the proposedwork utilized VGG-16 for feature extraction which forms the feature vectors.The features vectors are then provided for object detection using YOLOv4.Finally, the detected objects were tracked using Improved Unscented KalmanFilter (IUKF) and mapping the vehicles using time based mapping by considering their RFID, velocity, location, dimension and unique ID. The simulation ofthe proposed work is carried out using MATLAB R2020a simulation tool andperformance of the proposed work is compared with several metrics that showthat the proposed work outperforms than the existing works.

Examining the Optimization of Spray Cleaning Performance for LiDAR Sensor

Pollutants degrade the performance of LiDAR sensors used in autonomous vehicles. Therefore, there is an urgent need to develop cleaning technology for these sensors. In this study, a solid-state LiDAR sensor was selected as a target and sprayed/dried with 2.5 g of a mixture of Arizona dust and Kaolin. To achieve optimal LiDAR cleaning performance, the washer pressure, spray time, spray angle, and target point were selected as major variables. Additionally, an optimal cleaning solution for each spray was formed via the design of experiments and optimization techniques. Model suitability was observed for the second spray through to the fourth. The cleaning rate increased with the washer pressure and spray time. The influence of these variables decreased as the number of sprays increased. The spray angle and target point exhibited no significant influence, but excellent cleaning was observed in some central areas. Verification test results were within 3% for the second through fourth sprays, indicating reliability. This study used a designed experiment with 30 scenarios to reveal optimized conditions for protecting the sensor performance from external visibility obstructions. Disclosing the optimization method lowers the barrier for sensor cleaning manufacturers to develop their own technology, which ultimately enhances safer and more efficient autonomous driving.

Design of a metalens for beam collimation and angular amplification in optical phased array devices

We present an analytical design for increasing the beam sharpness (collimation) and field of view (FOV) of an optical phased array (OPA) device. In this work, a cylindrical metalens is used for collimation, while a set of metalens, with both concave and convex phase profiles, are incorporated to increase the FOV. Following the generalized vector law of reflection or refraction, the trajectories of the reflected or transmitted rays corresponding to the phase profile of phase gradient metasurfaces/metalens are obtained. Through the ray tracing method, the elliptical beam from the OPA device with a vertical beam (fast axis) width of 21 mm was collimated to a sharp spherical beam of width 1.5 mm by a metalens with a cylindrical phase profile. In addition, the incorporation of angular amplifier metalens with a 64-channel OPA device has shown an increased in FOV by almost threefold i.e., from 15 o to 41.96 o . Our results suggest that the use of metasurfaces/metalens can enhance the quality of output beam and provide significant advantages for compact on-chip integration with OPA devices in solid-state LiDAR applications.

Automated extrinsic calibration of solid-state frame LiDAR sensors with non-overlapping field of view for monitoring indoor stockpile storage facilities

Several industrial and commercial bulk material management applications rely on accurate, current stockpile volume estimation. Proximal imaging and LiDAR sensing modalities can be used to derive stockpile volume estimates in outdoor and indoor storage facilities. Among available imaging and LiDAR sensing modalities, the latter is more advantageous for indoor storage facilities due to its ability to capture scans under poor lighting conditions. Evaluating volumes from such sensing modalities requires the pose (i.e., position and orientation) parameters of the used sensors relative to a common reference frame. For outdoor facilities, a Global Navigation Satellite System (GNSS) combined with an Inertial Navigation System (INS) can be used to derive the sensors’ pose relative to a global reference frame. For indoor facilities, GNSS signal outages will not allow for such capability. Prior research has developed strategies for establishing the sensor position and orientation for stockpile volume estimation while relying on multi-beam spinning LiDAR units. These approaches are feasible due to the large range and Field of View (FOV) of such systems that can capture the internal surfaces of indoor storage facilities.The mechanical movement of multi-beam spinning LiDAR units together with the harsh conditions within indoor facilities (e.g., excessive humidity, wide range of temperature variation, dust, and corrosive environment in deicing salt storage facilities) limit the use of such systems. With the increasing availability of solid-state LiDAR units, there is an interest in exploring their potential for stockpile volume estimation. Despite their higher robustness to harsh conditions, solid-state LiDAR units have shorter distance measurement range and limited FOV when compared with multi-beam spinning LiDAR. This research presents a strategy for the extrinsic calibration (i.e., estimating the relative pose parameters) of installed solid-state LiDAR units inside stockpile storage facilities. The extrinsic calibration is made possible using deployed spherical targets and a complete, reference scan of the facility from another LiDAR sensing modality. The proposed research introduces strategies for: 1) automated extraction of the spherical targets; 2) automated matching of these targets in the solid-state LiDAR and reference scans using invariant relationships among them; and 3) coarse-to-fine estimation of the calibration parameters. Experimental results in several facilities have shown the feasibility of using the proposed methodology to conduct the extrinsic calibration and volume evaluation with an error percentage less than 3.5% even with occlusion percentages reaching up to 50%.

Apodized slanted teeth grating couplers for LiDAR applications

Solid state LiDAR systems traditionally rely on costly active components for efficient beam scanning. In this study, we propose a cost-effective, purely passive steering approach using apodized slanted grating couplers. Through apodization, we achieve a uniform upward emission profile and enhanced upward transmission. Theoretical calculations indicate successful steering of 91.5∘×42.8∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$91.5^\\circ \ imes 42.8^\\circ$$\\end{document}. Experimental results closely match theoretical predictions, validating the capabilities of our passive steering concept. Additionally, the grating couplers, with a length of 3 mm, enable a farfield FWHM of 0.026∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ$$\\end{document}, further enhancing sensing resolution.

Multitemporal Field-Based Maize Plant Height Information Extraction and Verification Using Solid-State LiDAR

Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize’s original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R2 value attained is 0.99. These metrics collectively attest to the method’s ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland.

VPE-SLAM: Virtual Point Enhanced SLAM Using Solid-State LiDAR for Weak Feature Environments

VPE-SLAM: Virtual Point Enhanced SLAM Using Solid-State LiDAR for Weak Feature Environments

A Greenhouse Gas Footprint Analysis of Advanced Hardware Technologies in Connected Autonomous Vehicles

Greenhouse gas emissions are a critical concern for China’s automotive industry, especially for passenger cars due to their high sales’ volume. Recently, the trend towards connected and autonomous driving vehicles has been significant in the passenger car market. However, the impact of these systems on the life cycle emissions of vehicles remains unclear. This paper focuses on system function levels from driver assistance to full driving automation and studies their life cycle greenhouse gas emissions. This research establishes a component list for the hardware system and a material inventory. Then, this paper reveals significant differences in total system emissions at these technology levels, 540.1 kg for primary, 1318.7 kg for medium, and 2279.2 kg for advanced systems. Despite this difference, the total is less than 7.23% of the total vehicle emissions. To further reduce this portion of GHG emissions, it is recommended that vehicles favor millimeter-wave radar over solid-state LiDAR in their sensing system hardware, coupled with cameras as the primary sensing element. In addition, Intelligent Hardware Systems are not recommended for internal combustion engine passenger cars for optimal balance between functionality and environmental impact.

PC-IDN: Fast 3D loop closure detection using projection context descriptor and incremental dynamic nodes

3D Loop Closure Detection (LCD) is a crucial technique for robotics, which can correct accumulated errors after long time exploration. Existing 3D LCD methods have been designed for mechanical LiDAR, limiting their performance for various solid-state LiDARs. In this paper, we propose a new LiDAR LCD method, PC-IDN, based on a novel descriptor called Projection Context (PC), and an efficient data structure named Incremental Dynamic Nodes (IDN). The PC considers side-view and describes scenes by leveraging distance and intensity values of the point clouds. Subsequently, the PC descriptors are collected in corresponding nodes for candidate scans selection. To ensure real-time performance, the IDN module dynamically adjusts the visited PCs in the database. The results demonstrate that our method is able to achieve better precision–recall and efficiency performance than existing 3D LCD methods.

Online Calibration of Extrinsic Parameters for Solid-State LIDAR Systems.

This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems-as required for calibration-is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 cm was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring.

LiDAR-Link: Observability-Aware Probabilistic Plane-Based Extrinsic Calibration for Non-Overlapping Solid-State LiDARs

LiDAR-Link: Observability-Aware Probabilistic Plane-Based Extrinsic Calibration for Non-Overlapping Solid-State LiDARs

A Novel Method for 3D Object Detection in Open-Pit Mine Based on Hybrid Solid-State LiDAR Point Cloud

In recent years, the mining industry has encountered challenges, such as a shortage of human resources, an ongoing emphasis on safety enhancements, and increased ecological preservation requirements. Autonomous mining trucks have emerged as a novel solution to effectively address these issues within open-pit mining operations. To meet the demanding conditions of open-pit mines, characterized by intense vibrations and extreme temperature variations, hybrid solid-state LiDAR has emerged as the primary choice for perception sensors. Recognizing the distinct data structure and distribution disparities between point clouds obtained through nonrepetitive scanning methods of hybrid solid-state LiDAR and traditional mechanical LiDAR, this paper proposed an innovative LiDAR 3D object detection model, PointPillars-HSL (PointPillars-Hybrid Solid-state LiDAR). This approach harmonizes the unique characteristics of open-pit mining environments and hybrid solid-state LiDAR point clouds. It optimizes the model’s preprocessing methodology, augments the dimensionality of pillar features, fine-tunes the loss function, and employs transfer learning techniques to reduce the reliance on specific datasets. The result is the effective deployment of a 3D object detection algorithm customized for hybrid solid-state LiDAR within the specific operational framework of open-pit mining. This achievement has yielded a noteworthy overall vehicle recognition rate of 89.72%.

Hierarchical fusion based high precision SLAM for solid-state lidar

Abstract Solid-state LiDARs have become an important perceptual device for simultaneous localization and mapping (SLAM) due to its low-cost and high-reliability compared to mechanical LiDARs. Nevertheless, existing solid-state LiDARs-based SLAM methods face challenges, including drift and mapping inconsistency, when operating in dynamic environments over extended periods and long distances. To this end, this paper proposes a robust, high-precision, real-time LiDAR-inertial SLAM method for solid-state LiDARs. At the front-end, the raw point cloud is segmented to filter dynamic points in preprocessing process. Subsequently, features are extracted using a combination of Principal Component Analysis (PCA) and Mean Clustering to reduce redundant points and improve data processing efficiency. At the back-end, a hierarchical fusion method is proposed to improve the accuracy of the system by fusing the feature information to iteratively optimize the LiDAR frames, and then adaptively selecting the LiDAR keyframes to be fused with the IMU. The proposed method is extensively evaluated using a Livox Avia solid-state LiDAR collecting datasets on two different platforms. In experiments, the end-to-end error is reduced by 35% and the single-frame operational efficiency is improved by 12% compared to LiLi-OM.

Solid-State LiDAR With Optimized Dynamic Beam Power Intensity for Reduced Thermal Load

Solid-State LiDAR With Optimized Dynamic Beam Power Intensity for Reduced Thermal Load

Identification Algorithm of Extrinsic Parameters Between 3-D Solid-State LiDAR and Mono-Camera

Identification Algorithm of Extrinsic Parameters Between 3-D Solid-State LiDAR and Mono-Camera

Small-Scale Rockfall Detection Method Based on Solid-State Lidar for Unstructured Transportation Roads in Open-Pit Mines

Small-Scale Rockfall Detection Method Based on Solid-State Lidar for Unstructured Transportation Roads in Open-Pit Mines

MCSGCalib: Multi-Constraint-Based Extrinsic Calibration of Solid-State LiDAR and GNSS/INS for Autonomous Vehicles

MCSGCalib: Multi-Constraint-Based Extrinsic Calibration of Solid-State LiDAR and GNSS/INS for Autonomous Vehicles

Mapping Research Based on Solid-State LiDAR Fusion with 2D LiDAR

Mapping Research Based on Solid-State LiDAR Fusion with 2D LiDAR

Wide Optical Beam Steering LCOS Device for Solid-State LiDAR Applications

Wide Optical Beam Steering LCOS Device for Solid-State LiDAR Applications