Single Light Detection And Ranging Research Articles

Ambient light immunity of a frequency-modulated continuous-wave (FMCW) LiDAR chip.

The interference between a frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) and other LiDARs or sunlight was theorized, considering the spatial overlap, frequency overlap, and intensity ratio. It has been concluded that the interference probability between LiDARs can be lower than a safety standard value for autonomous vehicles when the number of the resolution points of a single LiDAR is increased sufficiently and that the interference with incoherent sunlight does not occur. Due to the coherent detection of FMCW, such ambient light immunity is much better than time-of-flight LiDAR. The dependence of the interference on the wavelength range, sweep bandwidth, and sweep period was also observed experimentally using a silicon (Si) photonics FMCW LiDAR chip incorporating slow-light grating beam scanners. It was shown that the interference can be suppressed by increasing the number of resolution points and changing their common parameters moderately. Regarding the contamination of sunlight, unwanted beam shift due to heating was observed, although it will be suppressed simply by wavelength filtering.

Automated Method for SLAM Evaluation in GNSS-Denied Areas

The automated inspection and mapping of engineering structures are mainly based on photogrammetry and laser scanning. Mobile robotic platforms like unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), but also handheld platforms, allow efficient automated mapping. Engineering structures like bridges shadow global navigation satellite system (GNSS), which complicates precise localization. Simultaneous localization and mapping (SLAM) algorithms offer a sufficient solution, since they do not require GNSS. However, testing and comparing SLAM algorithms in GNSS-denied areas is difficult due to missing ground truth data. This work presents an approach to measuring the performance of SLAM in indoor and outdoor GNSS-denied areas using a terrestrial scanner Leica RTC360 and a tachymeter to acquire point cloud and trajectory information. The proposed method is independent of time synchronization between robot and tachymeter and also works on sparse SLAM point clouds. For the evaluation of the proposed method, three LiDAR-based SLAM algorithms called KISS-ICP, SC-LIO-SAM, and MA-LIO are tested using a UGV equipped with two light detection and ranging (LiDAR) sensors and an inertial measurement unit (IMU). KISS-ICP is based solely on a single LiDAR scanner and SC-LIO-SAM also uses an IMU. MA-LIO, which allows multiple (different) LiDAR sensors, is tested on a horizontal and vertical one and an IMU. Time synchronization between the tachymeter and SLAM data during post-processing allows calculating the root mean square (RMS) absolute trajectory error, mean relative trajectory error, and the mean point cloud to reference point cloud distance. It shows that the proposed method is an efficient approach to measure the performance of SLAM in GNSS-denied areas. Additionally, the method shows the superior performance of MA-LIO in four of six test tracks with 5 to 7 cm RMS trajectory error, followed by SC-LIO-SAM and KISS-ICP in last place. SC-LIO-SAM reaches the lowest point cloud to reference point cloud distance in four of six test tracks, with 4 to 12 cm.

High precision 3D imaging with timing corrected single photon LiDAR.

Single photon light detection and ranging (LiDAR) is an important technique for high precision long distance three-dimensional (3D) imaging. However, due to the effects and native limitations of system components, there exists ranging errors when using LiDAR system. For the LiDAR system that requires trigger detector to provide synchronization signals, the fluctuation of laser pulse energy causes the change of the initial time of the constant threshold triggered timing module, and subsequently leads to the ranging error. In this paper, we build a dual SPADs LiDAR system to avoid the ranging error caused by the fluctuation of laser pulse energy. By adding a reference optical path, the flight time of signal photons is corrected by reference photons, so as to realize the correction of ranging. A series of experiments demonstrate that the proposed LiDAR system has the capability of high precision ranging and 3D imaging. The system achieves range of error of 0.15 mm and range resolution of 0.3 mm at a distance of 29 m.

A spatial–spectral classification framework for multispectral LiDAR

ABSTRACT Precise classification of Light Detection and Ranging (LiDAR) point cloud is a fundamental process in various applications, such as land cover mapping, forestry management, and autonomous driving. Due to the lack of spectral information, the existing research on single wavelength LiDAR classification is limited. Spectral information from images could address this limitation, but data fusion suffers from varying illumination conditions and the registration problem. A novel multispectral LiDAR successfully obtains spatial and spectral information as a brand-new data type, namely, multispectral point cloud, thereby improving classification performance. However, spatial and spectral information of multispectral LiDAR has been processed separately in previous studies, thereby possibly limiting the classification performance of multispectral LiDAR. To explore the potential of this new data type, the current spatial–spectral classification framework for multispectral LiDAR that includes four steps: (1) neighborhood selection, (2) feature extraction and selection, (3) classification, and (4) label smoothing. Three novel highlights were proposed in this spatial – spectral classification framework. (1) We improved the popular eigen entropy-based neighborhood selection by spectral angle match to extract a more precise neighborhood. (2) We evaluated the importance of geometric and spectral features to compare their contributions and selected the most important features to reduce feature redundancy. (3) We conducted spatial label smoothing by a conditional random field, accounting for the spatial and spectral information of the neighborhood points. The proposed method demonstrated by a multispectral LiDAR with three channels: 466 nm (blue), 527 nm (green), and 628 nm (red). Experimental results demonstrate the effectiveness of the proposed spatial – spectral classification framework. Moreover, this research takes advantages of the complementation of spatial and spectral information, which could benefit more precise neighborhood selection, more effective features, and satisfactory refinement of classification result. Finally, this study could serve as an inspiration for future efficient spatial–spectral process for multispectral point cloud.

Optimal Clustering of Point Cloud by 2D-LiDAR Using Kalman Filter

Light detection and ranging (LiDAR) has been the primary sensor for autonomous mobility and navigation system owing to its stability. Although multiple-channel LiDAR (3D-LiDAR) can obtain dense point clouds that provide optimal performance for several tasks, the application scope is limited by its high-cost. When employing single channel LiDAR (2D-LiDAR) as a low-cost alternative, the quantity and quality of the point cloud cause conventional methods to perform poorly in clustering and tracking tasks. In particular, when handling multiple pedestrian scenarios, the point cloud is not distinguished and clustering is unable to succeed. Hence, we propose an optimized clustering method combined with a Kalman filter (KF) for simultaneous clustering and tracking applicable to 2D-LiDAR.

Center-Aware 3D Object Detection with Attention Mechanism Based on Roadside LiDAR

Infrastructure 3D Object Detection is a pivotal component of Vehicle-Infrastructure Cooperated Autonomous Driving (VICAD). As turning objects account for a high proportion of traffic at intersections, anchor-free representation in the bird’s-eye view (BEV) is more suitable for roadside 3D detection. In this work, we propose CetrRoad, a simple yet effective center-aware detector with transformer-based detection head for roadside 3D object detection with single LiDAR (Light Detection and Ranging). CetrRoad firstly utilizes a voxel-based roadside LiDAR feature encoder module that voxelizes and projects the raw point cloud into BEV with dense feature representation, following a one-stage center proposal module that initializes center candidates of objects based on the top N points in the BEV target heatmap with unnormalized 2D Gaussian. Then, taking attending center proposals as query embedding, a detection head with multi-head self-attention and multi-scale multi-head deformable cross attention can refine and predict 3D bounding boxes for different classes moving/parked at the intersection. Extensive experiments and analyses demonstrate that our method achieves state-of-the-art performance on the DAIR-V2X-I benchmark with an acceptable training time cost, especially for Car and Cyclist. CetrRoad also reaches comparable results with the multi-modal fusion method for Pedestrian. An ablation study demonstrates that center-aware query as input can provide denser supervision than a purified feature map in the attention-based detection head. Moreover, we were able to intuitively observe that in complex traffic environment, our proposed model could produce more accurate 3D detection results than other compared methods with fewer false positives, which is helpful for other downstream VICAD tasks.

3D Point Cloud Stitching for Object Detection with Wide FoV Using Roadside LiDAR

Light Detection and Ranging (LiDAR) is widely used in the perception of physical environment to complete object detection and tracking tasks. The current methods and datasets are mainly developed for autonomous vehicles, which could not be directly used for roadside perception. This paper presents a 3D point cloud stitching method for object detection with wide horizontal field of view (FoV) using roadside LiDAR. Firstly, the base detection model is trained by KITTI dataset and has achieved detection accuracy of 88.94. Then, a new detection range of 180° can be inferred to break the limitation of camera’s FoV. Finally, multiple sets of detection results from a single LiDAR are stitched to build a 360° detection range and solve the problem of overlapping objects. The effectiveness of the proposed approach has been evaluated using KITTI dataset and collected point clouds. The experimental results show that the point cloud stitching method offers a cost-effective solution to achieve a larger FoV, and the number of output objects has increased by 77.15% more than the base model, which improves the detection performance of roadside LiDAR.

Correcting MAGIC Telescope data taken under non-optimal atmospheric conditions with an elastic LIDAR

The Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes are a system of two Imaging Atmospheric Cherenkov Telescopes (IACTs). IACTs make calorimetric use of the Earth’s atmosphere, which allows them to reach large effective areas, but also makes them strongly dependent on the quality of the atmosphere at the time of the observations. Dust intrusions or clouds obscuring the observed Cherenkov light can then lead to a wrong reconstruction of the gamma-ray data. In order to mitigate this problem, the MPP group built and has been operating a single wavelength elastic LIDAR (LIght Detection And Ranging) system to perform real time ranged-resolved measurements of the aerosol transmission. This information is then used to quantify the quality of the telescope data, as well as to correct the data taken under suboptimal aerosol conditions. In this talk, the correction of atmospherically impaired IACT data will be described and the first systematic evaluation of the correction capabilities of the LIDAR system will be presented. The results describe the impact of the LIDAR corrections for a variety of atmospheric and observational conditions, and therefore contribute to a better understanding of the telescope’s performance and related systematic uncertainties.

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications.

2D FPCB micromirror for scanning LIDAR

This paper presents a 2D flexible printed circuit board (FPCB) micromirror and a scanning 3D light detection and ranging (LIDAR) based on it by integrating the 2D FPCB micromirror with a commercially available single point LIDAR. The 2D FPCB micromirror retains the benefits of previously developed 1D FPCB micromirrors, i.e. large aperture and low cost while providing rotation of the mirror plate about two orthogonal axes to be able to scan a laser beam about both vertical and horizontal axes to achieve 2D scanning. One 2D FPCB micromirror is integrated with a single point LIDAR to achieve a 3D scanning LIDAR, which, in comparison to the previously developed 1D FPCB micromirror based 3D LIDAR, achieved more compact structure and easier fabrication/assembly due to no strict requirement on the alignment between two micromirrors while only one 2D micromirror rather than two 1D micromirrors used. Prototypes of the 2D FPCB micromirror and the 3D LIDAR based on it are fabricated and tested. The test results demonstrate that the 2D FPCB micromirror based 3D LIDAR achieved a volume reduction over the previous 1D FPCB micromirror based 3D LIDAR from 1042 cm3 to 754 cm3 with a field of view of 40°× 24° at 150 Hz horizontal scanning and 2 Hz vertical scanning.

Precision Variable-Rate Spraying Robot by Using Single 3D LIDAR in Orchards

Automatic navigation (AN) is an essential component to ensure the safety of pesticide application in orchards, whereas precision variable-rate spraying (PVS) serves as an indispensable technology for reducing the application of pesticides and protecting the environment. At present, AN and PVS are not closely combined. In this case, a single three-dimension (3D) light detection and ranging (LIDAR) sensor is hereby adopted to sense the information of fruit trees around the robot and determine the region of interest (ROI). Moreover, two-dimensional (2D) processing is conducted over the point clouds within the ROI to obtain the center-of-mass coordinates of fruit trees, and determine the vertical distance of the robot to the center line of the fruit tree row (FTR) based on the FTR on both sides using the Random Sample Consensus (RANSAC) algorithm. Then, the robot is controlled to drive along the center line of the FTR. At the same time, the speed and position of the robot are determined by the encoder and inertial measurement unit (IMU), and the IMU corrects the information collected from the zoned canopy of the fruit trees. The results present a lateral deviation (LD) of less than 22 cm and a course deviation (CD) of less than 4.02° during AN. Compared with the traditional spraying (TS), the PVS applies 32.46%, 44.34% and 58.14% less pesticide application, air drift and ground loss, respectively. With the spraying effect guaranteed, the single 3D LIDAR, the encoder and IMU realize the AN and PVS of the robot, reduce the volume of pesticide application, ground loss and air drift, and effectively control the pollution caused by pesticides to the environment.

Mapping Dominant Boreal Tree Species Groups by Combining Area-Based and Individual Tree Crown LiDAR Metrics with Sentinel-2 Data

Airborne light detection and ranging (LiDAR) data are increasingly used to inform sustainable forest management practices. Information about species composition is needed for a range of applications; however, commonly used area-based summaries of LiDAR data are limited to accurately differentiate tree species. The objective of this study was to map dominant species groups across a large (>580,000 ha) boreal forest by combining area-based and individual tree metrics derived from single photon LiDAR data with multispectral information derived from Sentinel-2 imagery. Classification of the forest into jack pine (Pinus banksiana), black spruce (Picea mariana), mixed conifer, mixedwood, and hardwood species groups resulted in an overall accuracy of 67.8% compared with field data. A more generalized classification into softwood, hardwood, and mixedwood had an overall accuracy of 83.2%. The reflectance in the red edge region of the electromagnetic spectrum (λ = 740 nm), the area and volume of tree crowns, and the cumulative distribution of LiDAR returns in the canopy were particularly important variables to discriminate between species groups. Wall-to-wall predictions of species groups based on remotely sensed data—as derived herein—could provide a spatially-detailed alternative to stand-level species composition information traditionally derived from photo-interpreted aerial imagery.

Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds

Quantifying ecosystem structure is of key importance for ecology, conservation, restoration, and biodiversity monitoring because the diversity, geographic distribution and abundance of animals, plants and other organisms is tightly linked to the physical structure of vegetation and associated microclimates. Light Detection And Ranging (LiDAR) — an active remote sensing technique — can provide detailed and high resolution information on ecosystem structure because the laser pulse emitted from the sensor and its subsequent return signal from the vegetation (leaves, branches, stems) delivers three-dimensional point clouds from which metrics of vegetation structure (e.g. ecosystem height, cover, and structural complexity) can be derived. However, processing 3D LiDAR point clouds into geospatial data products of ecosystem structure remains challenging across broad spatial extents due to the large volume of national or regional point cloud datasets (typically multiple terabytes consisting of hundreds of billions of points). Here, we present a high-throughput workflow called ‘Laserfarm’ enabling the efficient, scalable and distributed processing of multi-terabyte LiDAR point clouds from national and regional airborne laser scanning (ALS) surveys into geospatial data products of ecosystem structure. Laserfarm is a free and open-source, end-to-end workflow which contains modular pipelines for the re-tiling, normalization, feature extraction and rasterization of point cloud information from ALS and other LiDAR surveys. The workflow is designed with horizontal scalability and can be deployed with distributed computing on different infrastructures, e.g. a cluster of virtual machines. We demonstrate the Laserfarm workflow by processing a country-wide multi-terabyte ALS dataset of the Netherlands (covering ∼34,000 km2 with ∼700 billion points and ∼ 16 TB uncompressed LiDAR point clouds) into 25 raster layers at 10 m resolution capturing ecosystem height, cover and structural complexity at a national extent. The Laserfarm workflow, implemented in Python and available as Jupyter Notebooks, is applicable to other LiDAR datasets and enables users to execute automated pipelines for generating consistent and reproducible geospatial data products of ecosystems structure from massive amounts of LiDAR point clouds on distributed computing infrastructures, including cloud computing environments. We provide information on workflow performance (including total CPU times, total wall-time estimates and average CPU times for single files and LiDAR metrics) and discuss how the Laserfarm workflow can be scaled to other LiDAR datasets and computing environments, including remote cloud infrastructures. The Laserfarm workflow allows a broad user community to process massive amounts of LiDAR point clouds for mapping vegetation structure, e.g. for applications in ecology, biodiversity monitoring and ecosystem restoration.

A Tightly-Coupled Framework for Large-Scale Map Construction With Multiple Non-Repetitive Scanning LiDARs

With the ability of providing direct and accurate enough range measurements, light detection and ranging (LiDAR) is playing an essential role in localization and detection for autonomous vehicles. Since single LiDAR suffers from hardware failure and performance degradation intermittently, we present a multi-LiDAR integration scheme in this article. Our framework tightly couples multiple non-repetitive scanning LiDARs with inertial, encoder, and global navigation satellite system (GNSS) into pose estimation and simultaneous global map generation. Primarily, we formulate a precise synchronization strategy to integrate isolated sensors, and the extracted feature points from separate LiDARs are merged into a single sweep. The fused scans are introduced to compute the scan-matching correspondences, which can be further refined by additional real-time kinematic (RTK) measurements. Based thereupon, we construct a factor graph along with the inertial preintegration result, estimated ground constraints, and RTK data. For the purpose of maintaining a restricted number of poses for estimation, we deploy a keyframe based sliding-window optimization strategy in our system. The real-time performance is guaranteed with multi-threaded computation, and extensive experiments are conducted in challenging scenarios. Experimental results show that the utilization of multiple LiDARs boosts the system performance in both robustness and accuracy.

Multitouch Pressure Sensing With Soft Optical Time-of-Flight Sensors

This article reports on optical time-of-flight (ToF) as a detection mode for soft optical deformation sensors. In this work, low-cost miniature light detection and ranging (LiDAR) chips recently developed for consumer mobile device applications perform real-time optical reflectometry at cm scales in soft optical waveguide networks. Our methods complement existing work by capturing spatial information that is lost with amplitude-only measurements. We demonstrate its application in a multitouch button system capable of extracting displacement values from soft optical switches with average error in the range of 100–200 microns on 1.75 mm of the switch travel. Because making reliable electronic connections to soft materials is a challenging task, we focus on minimizing connector count. In a tracking application, we find the centroid of an object on a three-point supported plate using three optical pressure switches connected to a single LiDAR sensor.

Classification of raw LiDAR point cloud using point-based methods with spatial features for 3D building reconstruction

Building extraction from light detection and ranging (LiDAR) data for 3-dimensional (3D) reconstruction requires accurately classified LiDAR points. In recent years, approaches developed for the classification mostly based on gridded LiDAR data. In the gridding process of LiDAR data, there is a characteristic point loss which results in reduced height accuracy. The effect of such loss can be eliminated using classified raw LiDAR data. In this study, an automatic point-based classification approach for raw LiDAR data classification with spatial features has been proposed for 3D building reconstruction. Using spatial features, the hierarchical rules have been determined. The spatial features, such as height, the local environment, and multi-return, of the LiDAR points were analyzed, and every single LiDAR points automatically assigned to the classes based on these features. The proposed classification approach based on raw LiDAR data had an overall accuracy of 79.7% in the test site located in Istanbul, Turkey. Finally, 3D building reconstruction was performed using the results of the proposed automatic point-based classification approach.

Research on the Influence of Metamaterials on Single Photon LiDAR

Single photon light detection and ranging (LiDAR) has the advantages of high angle and distance resolution, great concealment, a strong anti-active jamming capability, small volume, and light mass, and has been widely applied in marine reconnaissance, obstacle avoidance, chemical warfare agent detection, and navigation. With the rapid development of metamaterials, the performance of a single photon LiDAR system would be improved by optimizing the core devices in the system. In this paper, we first analyzed the performance index of the single photon LiDAR and discovered the potential of metamaterials in improving the system performance. Then, the influence of metamaterials on the core devices of the single photon LiDAR were discussed, including lasers, scanning devices, optical lenses, and single photon detectors. As a result, we have concluded that through effective light field modulation, metamaterial technology might enhance the performance innovation of the single photon LiDAR.

Coastal Wind Measurements Using a Single Scanning LiDAR

A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR was deployed on the landward end of the HORS pier. We compared the wind speed and direction data recorded by the scanning LiDAR to the observations obtained from a vertical profiling LiDAR installed at the opposite end of the pier, 400 m from the scanning LiDAR. The best practice for offshore wind measurements using a single scanning LiDAR was evaluated by comparing results from a total of nine experiments using several different scanning settings. A two-parameter velocity volume processing (VVP) method was employed to retrieve the horizontal wind speed and direction from the radial wind speed. Our experiment showed that, at the current offshore site with a negligibly small vertical wind speed component, the accuracy of the scanning LiDAR wind speeds and directions was sensitive to the azimuth angle setting, but not to the elevation angle setting. In addition to the validations for the 10-minute mean wind speeds and directions, the application of LiDARs for the measurement of the turbulence intensity (TI) was also discussed by comparing the results with observations obtained from a sonic anemometer, mounted at the seaward end of the HORS pier, 400 m from the scanning LiDAR. The standard deviation obtained from the scanning LiDAR measurement showed a greater fluctuation than that obtained from the sonic anemometer measurement. However, the difference between the scanning LiDAR and sonic measurements appeared to be within an acceptable range for the wind turbine design. We discuss the variations in data availability and accuracy based on an analysis of the carrier-to-noise ratio (CNR) distribution and the goodness of fit for curve fitting via the VVP method.

Towards a novel auto-rotating lidar platform for cavity surveying

This paper presents the conceptual design, construction, and preliminary testing of a novel auto-rotating platform for cavity surveying. Cavity surveying involves generating a 3D model of an opening from acquired point cloud data. 3D models are used for volume estimation, stope reconciliation, dilution control, convergence monitoring, and rock mass stability analysis. In contrast to conventional cavity monitoring systems (CMS), which employ a rotating light detection and ranging (LiDAR) sensor attached to a stationary boom, the presented novel platform consists of a single point LiDAR sensor attached to an auto-rotating body as a unique solution to the cavity surveying problem. A proof of concept device consisting of a four-blade rotor and a data acquisition system was constructed and dropped from a five-metre overhang. Although auto-rotation was not reached, the set of indoor drop tests provide preliminary results that illustrate the design concept and describes the impact of design parameters. .

Learning-Based Human Segmentation and Velocity Estimation Using Automatic Labeled LiDAR Sequence for Training

In this paper, we propose an automatic labeled sequential data generation pipeline for human segmentation and velocity estimation with point clouds. Considering the impact of deep neural networks, state-of-the-art network architectures have been proposed for human recognition using point clouds captured by Light Detection and Ranging (LiDAR). However, almost all conventional datasets are either a collection of single LiDAR scanning with label information or sequential LiDAR scanning without label information. This limitation has disturbed the progress of research to date. Therefore, we have developed an automatic labeled sequential data generation pipeline, in which we can control any parameter or data generation environment with pixel-wise and per-frame ground truth segmentation and pixel-wise velocity information for human recognition. Our approach uses a precise human model and reproduces a precise motion to generate realistic artificial data. We present more than 7K sequences, where each sequence consists of 32 frames generated by the proposed pipeline. With the proposed sequence generator, we confirm that human segmentation performance is improved when using the sequential data compared to when using the data from a single LiDAR scan. We also evaluate our data by comparing with data generated under different conditions. In addition, we estimate pedestrian velocity with LiDAR by only utilizing data generated by the proposed pipeline.