Light Detection System Research Articles

Special Issue on Robotics and Mechatronics Technology for Aerial Robots

Aerial robot technologies, such as drones and unmanned aerial vehicles, are rapidly advancing in various domains. These robots now possess enhanced autonomous flying abilities and use sensor technology, Global Navigation Satellite System (GNSS), Light Detection and Ranging (LiDAR) system, and cameras to avoid obstacles, automatically land, and track flights. Evolving technologies in airspace management and route optimization ensure that flights are efficient and safe. Aerial robots have a wide range of applications, such as agriculture, disaster response, environmental monitoring, and inspection. These devices excel at precise data collection thanks to their high-resolution cameras and high-precision sensors. Swarm robotics, which allows collaboration between multiple aerial and ground robots, is also progressing. This enables quick coverage of large areas, which benefits disaster relief and land management. In addition, research and development of flying robots modeled after flying animals is underway. The evolution of aerial robots results in their efficient use in a variety of fields and new applications. This special issue includes the latest research papers and development reports on aerial robotic technology from the aforementioned perspectives. We hope that this special issue will pique researchers’ and engineers’ interest in aerial robots, thereby encouraging additional research and development. Finally, we would like to express our gratitude to all authors, all reviewers, the editorial board of the Journal of Robotics and Mechatronics, and Fuji Technology Press Ltd.

SiPM-based LiDAR with multipulse sequence modulation and multithreshold signal processing.

A light detection and range technology (LiDAR) system that enables rapid ranging under extremely low signal-to-noise ratios (SNRs) during daylight conditions based on a SiPM (silicon photomultiplier) detector is proposed. The system emits a sequence of modulated laser pulses by controlling the semiconductor laser and then processes the SiPM response signals using dynamic multithreshold. The experimental result shows that, under extremely low SNR of daylight, the system achieves a 100% success rate in continuous ranging of a low reflectivity target at 125 m, with a ranging precision of less than 20 cm and a ranging time of less than 10 µs. Our system provides a significant reference value for achieving high-speed, noise-resistant, miniaturized, practical, and low-cost LiDAR based on a SiPM.

Retraction Note: Application of light detection system based on fuzzy decision algorithm in motion data collection and motion monitoring

Retraction Note: Application of light detection system based on fuzzy decision algorithm in motion data collection and motion monitoring

Mobile laser scanning as reference for estimation of stem attributes from airborne laser scanning

The acquisition of high-quality reference data is essential for effectively modelling forest attributes. Incorporating close-range Light Detection and Ranging (LiDAR) systems into the reference data collection stage of remote sensing-based forest inventories can not only increase data collection efficiency but also increase the number of attributes measured with high quality. Therefore, we propose a model-based forest inventory method that uses reference data collected by a car-mounted mobile laser scanning (MLS) system along boreal forest roads. This approach is used for the estimation of diameter at breast height (DBH) and stem volume at the individual tree-level from airborne laser scanning (ALS) data. In addition, we compare the estimates obtained using the proposed method with the ones derived from reference data collected by traditional field inventory of 265 field plots systematically distributed over the study area. The accuracy of the estimates remained comparable regardless of the reference dataset used for estimation of DBH and stem volume. When using the field inventory dataset for model training, the root mean square error (RMSE) of DBH estimates were 4.06 cm (18.8 %) for Norway spruce trees, 6.3 cm (29.6 %) for Scots pine and 8.61 cm (55.9 %) for deciduous trees. Similarly, when evaluating predictions based on the MLS dataset as reference, RMSEs were equal to 3.97 cm (18.4 %) for Norway spruce, 6.12 cm (28.8 %) for Scots pine, and 8.98 cm (58.3 %) for deciduous trees. In general, biases were below 1 cm for most species classes, with the exception of deciduous trees. The accuracy of stem volume also had RMSEs varying across different tree species. For the estimates based on traditional field inventory, the RMSEs were 0.176 m3 (38.8 %) for Norway spruce, 0.228 m3 (52.4 %) for Scots pine and 0.246 m3 (158 %) for deciduous trees. When using the MLS dataset as a reference, the RMSEs were equal to 0.176 m3 (38.8 %), 0.228 m3 (52.4 %), and 0.246 m3 (158 %) for Norway spruce, Scots pine, and deciduous trees, respectively. Car-mounted MLS demonstrated its potential as an efficient alternative for collecting reference data in remote sensing-based forest inventories, which could complement traditional methods.

Locally robust Msplit estimation

Abstract Processing measurement data is an essential part of surveying engineering. One can list several methods in such a context: least squares estimation, M-estimation, R-estimation, etc. Some methods were developed by surveyors, e.g., the Danish method, IGG scheme, or Msplit estimation. The last method is, in fact, a class of estimation procedures dedicated to different problems. As a new approach to processing data, Msplit estimation is still being developed and improved. That paper concerns the local robustness of Msplit estimation and introduces a new Msplit estimation variant that is less sensitive to local outliers. Such a property seems important, especially in big data processing, such as observations from Light Detection and Ranging systems. The new variant modifies the squared Msplit estimation (SMS estimation) by implementing the adapted Tukey weight function, hence its acronym SMSTL estimation. The basic theoretical and empirical analyses, which were performed for the univariate model using, among others, the appropriate measures of robustness, confirmed the expected property of the method. The further tests, based on simulated as well as real data, show that the new method might overperform other Msplit estimation variants and classical methods for the chosen types of observation sets.

Advancements in Dual-Function UV-Visible Light Detection

Abstract This research introduces a photodetector capable of differentiating between ultraviolet (UV) and visible light, employing the unique properties of zinc oxide (ZnO) and α-silicon (α-Si). The core of this innovative device is a pixel unit circuit, designed with three integral components: a delay circuit for precise timing, a photosensitive element composed of an α-Si thin film resistor and a ZnO thin film transistor (ZnO TFT) for respective light spectrum detection, and a source follower for effective signal transmission. Through simulations conducted in Cadence Virtuoso, the device’s responsiveness to varying intensities of UV and visible light was assessed. Adjustments to the α-Si resistance and ZnO TFT threshold voltage simulated realistic light conditions, demonstrating the device’s capability to generate distinct voltage changes in response to different light spectrums. These results underline the potential of the design for larger-scale implementations and highlight its adaptability and precision in detecting diverse light intensities. This research provides the basis for the development of simultaneous UV-visible detection and scalable light detection systems. In addition, taking advantage of the properties of ZnO TFT, which can also be used in the future in curved transparent environments.

Modeling of solar radiation and sub-canopy light regime on forest inventory plots of mixed conifer and deciduous temperate forests using point clouds from personal laser scanning

Solar radiation is a major driver of forest ecosystems and affects the hydrological cycle and energy balance. The vitality of trees strongly depends on the amount of light input to individual trees, and successful forest regeneration demands a certain level of solar radiation. Therefore, characterization of the light regime inside a plant canopy (i.e., quantification and timing of light penetration) is of particular importance. Because these parameters greatly depend on the 3D structure of the forest canopy, Light Detection and Ranging (LiDAR) systems provide suitable technology for solving this task. In this study, an algorithm was developed to estimate the amount of potential solar radiation reaching the forest floor from a point cloud collected in a temperate mixed forest located in Lanzenkirchen (Lower Austria) using terrestrial LiDAR. The path length through the canopy was calculated for 40 forest inventory plots as the distance between the first and last canopy hit along a sun ray, that is, for each day during the vegetation period at an hourly resolution. This distance was easily derived from the z-coordinates of the point cloud after transforming the latter into a coordinate system in which the z-axis ran parallel to the sun rays. Applying Beer’s law, the amount of solar radiation on the forest floor was expressed as a function of the solar radiation above the canopy and the path length through the canopy. To validate the results, we used two sets of reference data: (1) hemispherical photographs taken at the same plots, using a camera on a self-levelling mount, which were processed with the HemiView software, and (2) data from a Solariscope survey. Both the Solariscope and HemiView software calculate below-canopy radiation as a percentage of above-canopy radiation, joining direct and diffuse radiation (Total Site Factor, TSF). The TSF values obtained from our modeling procedure were consistent with the Solariscope (R²=0.61) and HemiView (R²=0.71) results for the entire growing season. Because modern LiDAR-based forest inventories provide all the necessary data for our light modeling approach (the point cloud, the geographical position, and a record of the occurring tree species) without additional requirements, together with the easy applicability of the algorithm, this tool is a promising asset for ecological and silvicultural activities.

Effective Training and Inference Strategies for Point Classification in LiDAR Scenes

Light Detection and Ranging systems serve as robust tools for creating three-dimensional representations of the Earth’s surface. These representations are known as point clouds. Point cloud scene segmentation is essential in a range of applications aimed at understanding the environment, such as infrastructure planning and monitoring. However, automating this process can result in notable challenges due to variable point density across scenes, ambiguous object shapes, and substantial class imbalances. Consequently, manual intervention remains prevalent in point classification, allowing researchers to address these complexities. In this work, we study the elements contributing to the automatic semantic segmentation process with deep learning, conducting empirical evaluations on a self-captured dataset by a hybrid airborne laser scanning sensor combined with two nadir cameras in RGB and near-infrared over a 247 km2 terrain characterized by hilly topography, urban areas, and dense forest cover. Our findings emphasize the importance of employing appropriate training and inference strategies to achieve accurate classification of data points across all categories. The proposed methodology not only facilitates the segmentation of varying size point clouds but also yields a significant performance improvement compared to preceding methodologies, achieving a mIoU of 94.24% on our self-captured dataset.

Improving the Accuracy of Digital Terrain Models Using Drone-Based LiDAR for the Morpho-Structural Analysis of Active Calderas: The Case of Ischia Island, Italy

Over the past two decades, the airborne Light Detection and Ranging (LiDAR) system has become a useful tool for acquiring high-resolution topographic data, especially in active tectonics studies. Analyzing Digital Terrain Models (DTMs) from LiDAR exposes morpho-structural elements, aiding in the understanding of fault zones, among other applications. Despite its effectiveness, challenges persist in regions with rapid deformation, dense vegetation, and human impact. We propose an adapted workflow transitioning from the conventional airborne LiDAR system to the usage of drone-based LiDAR technology for higher-resolution data acquisition. Additionally, drones offer a more cost-effective solution, both in an initial investment and ongoing operational expenses. Our goal is to demonstrate how drone-based LiDAR enhances the identification of active deformation features, particularly for earthquake-induced surface faulting. To evaluate the potential of our technique, we conducted a drone-based LiDAR survey in the Casamicciola Terme area, north of Ischia Island, Italy, known for the occurrence of destructive shallow earthquakes, including the 2017 Md = 4 event. We assessed the quality of our acquired DTM by comparing it with existing elevation datasets for the same area. We discuss the advantages and limitations of each DTM product in relation to our results, particularly when applied to fault mapping. By analyzing derivative DTM products, we identified the fault scarps within the Casamicciola Holocene Graben (CHG) and mapped its structural geometry in detail. The analysis of both linear and areal geomorphic features allowed us to identify the primary factors influencing the current morphological arrangement of the CHG area. Our detailed map depicts a nested graben formed by two main structures (the Maio and Sentinella faults) and minor internal faults (the Purgatorio and Nizzola faults). High-resolution DEMs acquired by drone-based LiDAR facilitated detailed studies of the geomorphology and fault activity. A similar approach can be applied in regions where the evidence of high slip-rate faults is difficult to identify due to vegetation cover and inaccessibility.

Template-Confined Oriented Perovskite Nanowire Arrays Enable Polarization Detection and Imaging.

Polarized light detection can effectively identify the difference between the polarization information on the target and the background, which is of great significance for detection in complex natural environments and/or extreme weather. Generally, polarized light detection inevitably relies on anisotropic structures of photodetector devices, while organic-inorganic hybrid perovskites are ideal for anisotropic patterning due to their simple and efficient preparation by solution method. Compared to patterned thin films, patterned arrays of aligned one-dimensional (1D) perovskite nanowires (PNWAs) have fewer grain boundaries and lower defect densities, making them well suited for high-performance polarization-sensitive photodetectors. Here, we fabricated PNWAs crystallographically aligned with variable line widths and alignment densities employing CD-ROM and DVD-ROM grating pattern template-confined growth (TCG) methods. The photodetectors constructed from MAPbI3 PNWAs achieved responsivity of 35.01 A/W, detectivity of 6.85 × 1013 Jones, and fast response with a rise time of 172 μs and fall time of 114 μs. They were successfully applied to high-performance polarization detection with a polarization ratio of 1.81, potentially applicable in polarized light detection systems.

Light detection and cosmic rejection in the ICARUS LArTPC at Fermilab

The ICARUS-T600 detector is a 760-ton Liquid Argon Time Projection Chamber (LArTPC) currently operating at Fermilab as the Far Detector in the Short Baseline Neutrino (SBN) program. The SBN program is composed of three LArTPCs with a central goal of testing the sterile neutrino hypothesis. After operating for 3-years in the Gran Sasso Underground Laboratory, the ICARUS detector was shipped to CERN where it was outfitted with 360 8” Photomultiplier Tubes (PMTs) for a new optical detection system. The PMT system detects fast scintillation light from charged particles interacting in the Liquid Argon, generating the trigger signal for the full detector and allows 3D reconstruction of events. Now operating at shallow depth, the detector is exposed to a high flux of cosmic rays that can fake neutrino interactions. To mitigate this effect a Cosmic Ray Tagger (CRT) and a 3-meter-thick concrete were installed. Precise timing information from both the PMT and CRT subsystems can help to identify whether an interaction originated from inside or outside of the ICARUS cryostat. This paper reviews a method for cosmogenic background reduction and timing calibration of the CRT and PMT light detection systems in ICARUS.

Solar Tracking System

Abstract: The usage of solar panels to convert solar energy into electrical energy has gr own in recent years. The solar panel can be utilized as a huge solar system that is connected to the electrical grids or as a standalone system. The daily energy consumption of our planet is approximately 12 Terawatts, whereas the earth receives 84 Terawatts of power. We are attempting to use solar panels to harness more solar energy. To optimize solar energy conversion to electrical power, solar panels must be oriented perpendicular to the sun. Therefore, it's crucial to track the sun's location and align the solar panel. The purpose of this project is to create an automated system that can determine the sun's lo cation. For optimal energy conversion at all times, the tracking system will adjust the solar panel so that it is perpendicular to the sun. In this system, sensor s made of photoresistors will be employed. The system will include a solar panel, a microcontroller, a gear motor system, and a light detection system.

Status and perspectives of the ICARUS experiment at the Fermilab Short Baseline Neutrino program

The ICARUS collaboration has employed the 760 t T600 detector in a successful three-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous νe appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around Δm2 ∼ 1 eV2. After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. In 2020 the cryogenic commissioning began with detector cool down, liquid Argon filling and recirculation. ICARUS then started its operation collecting the first neutrino events from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis, which were used to test the ICARUS event selection, reconstruction and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022, moving then to data taking for neutrino oscillation physics, aiming at first to either confirm or refute the claim by Neutrino-4 short-baseline reactor. ICARUS will also jointly search for evidence of sterile neutrinos together with the Short-Baseline Near Detector, within the Fermilab Short-Baseline Neutrino program experiment, and will perform measurements of neutrino cross sections with both beams and several Beyond Standard Model searches with the NuMI beam. In this paper, the main technical achievements of the ICARUS detector subsystems (Time Projection Chambers, Light Detection System, Cosmic Ray Tagger, Trigger and Data Acquisition) obtained with both BNB and NuMI neutrino beams during the commissioning phase, will be presented in terms of the overall detector performance and capability to select and reconstruct neutrino events.

High precision rail surface obstacle detection algorithm based on 3D imaging LiDAR

Railway perimeter intrusion detection is of great significance for ensuring railway safety operations. In this paper, a Light Detection and Ranging (LiDAR) system is designed and developed based on the specific environmental requirements of railway applications. The system is capable of autonomously adjusting the imaging range and angular resolution, enabling high-precision imaging within a 50 m monitoring area. Additionally, based on registration and a series of filtering algorithms, a railway surface obstacle extraction algorithm is developed. The algorithm achieves a 100 % detection rate for obstacles with a minimum height of 15 cm at any position within the monitoring area, and a 70 % detection rate for obstacles with a height of 10 cm. In this algorithm, a coarse registration method based on global planar features combined with the Iterative Closest Point (ICP) registration algorithm achieves sub-centimeter registration accuracy for consecutive point cloud frames. The filtering algorithm based on local planar features enables precise extraction of geometric changes in the railway surface, with simple calculations and fast processing speed, making it highly valuable for extracting object edge features.

Sounding out the dynamics: a concise review of high-speed photoacoustic microscopy.

Photoacoustic microscopy (PAM) offers advantages in high-resolution and high-contrast imaging of biomedical chromophores. The speed of imaging is critical for leveraging these benefits in both preclinical and clinical settings. Ongoing technological innovations have substantially boosted PAM's imaging speed, enabling real-time monitoring of dynamic biological processes. This concise review synthesizes historical context and current advancements in high-speed PAM, with an emphasis on developments enabled by ultrafast lasers, scanning mechanisms, and advanced imaging processing methods. We examine cutting-edge innovations across multiple facets of PAM, including light sources, scanning and detection systems, and computational techniques and explore their representative applications in biomedical research. This work delineates the challenges that persist in achieving optimal high-speed PAM performance and forecasts its prospective impact on biomedical imaging. Recognizing the current limitations, breaking through the drawbacks, and adopting the optimal combination of each technology will lead to the realization of ultimate high-speed PAM for both fundamental research and clinical translation.

Time calibration and synchronization of the scintillation light detection system in ICARUS-T600

The ICARUS-T600 Liquid Argon (LAr) Time Projection Chamber (TPC) is presently taking data in the Short Baseline Neutrino (SBN) program at Fermilab (U.S.A.) to search for a possible LSND-like sterile neutrino signal at Δm 2 ≈ 1 eV2 with the Booster Neutrino Beam (BNB). A light detection system, based on 360 large area Photo-Multiplier Tubes (PMTs), has been realized for ICARUS-T600 to detect VUV photons produced by the passage of ionizing particles in LAr. This system is fundamental for the TPC operation, providing an efficient trigger and contributing to the 3D reconstruction of events. Moreover, since the detector is exposed to a huge flux of cosmic rays due to its shallow depths installation, the light detection system allows for the time reconstruction of events, contributing to the identification and to the selection of genuine neutrino interactions. The correct time reconstruction of events requires the precise knowledge of the delay of each PMT channel and a good synchronization of recording electronics, this last based on fast sampling digitizers. To achieve a time resolution better than 1 ns, we perform three consecutive timing corrections deployed at different stages of the optical data flow. Results demonstrate the capability of the ICARUS-T600 light detection system to allow a precise reconstruction of the temporal evolution of each event occurring in the detector and the association of neutrino events with the bunched structure of BNB.

Extrinsic Calibration of Camera and LiDAR Systems With Three‐Dimensional Towered Checkerboards

With the increasing utilization of cameras and three‐dimensional Light Detection and Ranging (LiDAR) systems in perception tasks, the fusion of these two sensor modalities has emerged as a prominent research focus in the fields of robotics and unmanned systems. While various extrinsic calibration methods have been developed, they often suffer from limited accuracy when using low‐resolution LiDAR sensors and require the placement of calibration targets at multiple locations. This paper introduces a novel calibration target known as the Three‐Dimensional Towered Checkerboard (3TC), along with a precise and straightforward extrinsic calibration approach for camera‐LiDAR systems. The 3TC consists of stacked cubes adorned with planar or 2D checkerboards, which provide the known positions of checkerboard corner points in three‐dimensional space. Leveraging the Iterative Closest Point (ICP) algorithm, the proposed method calculates the spatial relationship between LiDAR point cloud data and the 3TC model to infer the positions of checkerboard corner points in the LiDAR coordinate system. Subsequently, the Perspective‐n‐Point (PnP) algorithm is employed to establish the correlation between corner positions in the LiDAR coordinate system and the camera image, given the intrinsic parameters of the camera. By ensuring an adequate number of cubes and 2D checkerboards on a specific 3TC, along with accurately estimated corner point positions in LiDAR, a single frame of data from both the camera and LiDAR facilitates their extrinsic calibration. Experimental validations conducted across diverse camera and LiDAR systems, achieving minimal error close to the theoretical limit of the devices, attest to the robustness and precision of the 3TC and the proposed calibration methodology.

Application of light detection system based on fuzzy decision algorithm in motion data collection and motion monitoring

Application of light detection system based on fuzzy decision algorithm in motion data collection and motion monitoring

SEMANTIC SEGMENTATION OF UAV LIDAR DATA FOR TREE PLANTATIONS

Abstract. Tree plantations, characterized by large-scale cultivation of trees with high commercial values, often rely on accurate inventory data to improve their capacity. However, understanding tree plantations with different components on a large scale for growth prediction is still a tricky problem. In this paper, we harness the power of Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) systems to acquire 3D point clouds of tree plantations and investigate the potential of deep learning segmentation for enhanced understanding of plantation UAV LiDAR point clouds, thereby promoting precision forest management. Two datasets from the same plantation without debris on the ground and with harvested debris were tested. Experimental results showed that we were able to process a plantation consisting of 300 trees in 2 min and achieve an overall accuracy of 95% segmentation for this plantation. This research demonstrates the feasibility of the deep learning method in segmenting large-scale tree plantation point clouds, which is able to speed up the inventory of tree plantations.

Flexible top-illuminated organic photodetector using an ultrathin-metal-based transparent electrode

Top-illuminated structure facilitates the integration of organic photodetectors (OPDs) into high-resolution flexible wearable light detection systems by allowing the OPDs to be deposited on the bottom readout circuit. However, constructing this structure poses a challenge as it demands metallic electrodes with both high optical transparency and high electrical conductivity. But to achieve practical sheet resistances, most semitransparent metallic electrodes tend to reflect a large portion of incident light instead of allowing it to be absorbed by the photoactive layer of the OPDs. This, in turn, results in reduced photocurrent generation. To address this issue, a semiconducting germanium (Ge) film is introduced into a sliver (Ag) film, effectively reducing its reflectivity by lessening scattering. The Ge film also changes how the Ag film grows, further reducing its absorption by lowering the critical thickness needed for forming a continuous film. This approach yields a 10 nm metallic electrode with a transmittance of 70%, a reflectivity of 12%, and a sheet resistance of 35.5 Ω/□. Using this metallic electrode, flexible OPDs exhibit a high photo-to-dark current ratio of 2.9×104 and improved mechanical properties. This finding highlights the benefits of the top-illuminated structure, which effectively reduces losses caused by waveguided modes of the incident light.